Employee Engagement Analysis for Hooli
Daehyeon (Dae) Kim
2024-04-25
1 OVERVIEW OF THIS DOCUMENT
This document shows my R codes that I used to prepare and analyze
Hooli’s employee survey data. Please use the table of content on the
left side of this document to navigate the document. If you click “Show”
button in each section, you can see my codes. The document is structured
as follows:
- Overview of this document
- Data preparation & processing for analysis
- Data Analysis
- 3.1 Calculate the favorable scores for all questions and factors for Hooli overall
- 3.2 Calculate engagement factor (favorable) scores for all tenure groups
- 3.3 Calculate favorable scores for all questions and factors by gender
- 3.4 Correlate all on-engagement questions to the engagement factor for Hooli overall
- 3.5 Calculate engagement & leadership factor (favorable) scores for all countries
- 3.6 Demographic Composition of the Company
2 DATA PREPARATION & PROCESSING FOR ANALYSIS
2.1 Loading Data
#check and install required packages to successfully run this rmd
required_packages <- c(
"tidyselect", "xfun", "corrplot", "pander", "reshape2", "purrr", "tcltk", "colorspace",
"vctrs", "summarytools", "generics", "htmltools", "yaml", "grDevices", "base64enc", "utf8",
"rlang", "pillar", "foreign", "glue", "withr", "pryr", "readxl", "matrixStats",
"lifecycle", "plyr", "NCmisc", "stringr", "munsell", "gtable", "cellranger", "codetools",
"evaluate", "labeling", "knitr", "fastmap", "fansi", "methods", "Rcpp", "backports",
"scales", "checkmate", "magick", "farver", "rapportools", "ggplot2", "stats", "datasets",
"graphics", "digest", "stringi", "dplyr", "grid", "cli", "tools", "magrittr",
"tibble", "Amelia", "tidyr", "pkgconfig", "MASS", "utils", "timechange", "lubridate",
"rmarkdown", "base", "rstudioapi", "R6", "compiler"
)
for (pkg in required_packages) {
if (!(pkg %in% installed.packages())) {
install.packages(pkg, dependencies = TRUE)
}
}
#load the data
library(readxl)
df_original <- read_xlsx("data/sr._people_science_analyst_assignment_dataset_2024.xlsx")
2.2 Data Exploration & Preparation
2.2.1 Examining the macro features of the data
I first check the macro features of this df to make sure that everything is correct.
2.2.1.1 transforming ‘lea_3’ into a numeric variable
‘lea_3’ should be a numeric variable. After checking the data, employee ID M01562’s response is only missing for’lea_3’. Therefore, this ‘N/A’ should be recoded as -1.
2.2.1.2 transforming’hiredate’ into a date variable
‘hiredate’ should be a date variable. It is also important to note that each country follows a different date format (it’s a good practice to check the data in Excel before loading in into R to prevent such a mistake). So, in converting this variable to date-type, I need to specify this format in my code.
- Australia: Day/Month/Year
- Denmark: Day/Month/Year
- France: Day/Month/Year
- Germany: Day/Month/Year
- India: Day/Month/Year
- UK: Day/Month/Year
- United Kingdom: Day/Month/Year
- United States: Month/Day/Year
- USA: Month/Day/Year
- Canada: Month/Day/Year
- China: Year/Month/Day
# checking the macro-level trends of the dataset
head(df_original, 5)tail(df_original, 5)str(df_original)## tibble [2,651 × 27] (S3: tbl_df/tbl/data.frame)
## $ eeid : chr [1:2651] "M01434" "M00631" "M00325" "M00805" ...
## $ ali_1 : num [1:2651] 5 4 5 5 5 4 4 5 5 5 ...
## $ ali_2 : num [1:2651] 5 4 5 5 5 4 3 5 5 5 ...
## $ ali_3 : num [1:2651] 4 4 4 5 4 3 4 4 4 5 ...
## $ col_1 : num [1:2651] 5 4 5 5 5 4 3 4 5 5 ...
## $ col_2 : num [1:2651] 3 4 4 5 5 4 3 5 4 4 ...
## $ col_3 : num [1:2651] 1 4 5 5 5 4 3 5 4 5 ...
## $ eng_1 : num [1:2651] 5 4 5 5 5 4 4 5 5 5 ...
## $ eng_2 : num [1:2651] 5 4 5 5 5 4 3 4 5 5 ...
## $ eng_3 : num [1:2651] 5 4 5 5 5 3 4 5 5 4 ...
## $ eng_4 : num [1:2651] 5 3 4 5 4 4 3 4 5 5 ...
## $ eng_5 : num [1:2651] 5 3 3 5 3 4 3 5 5 3 ...
## $ inc_1 : num [1:2651] 5 4 5 5 5 4 4 5 5 5 ...
## $ inc_2 : num [1:2651] 5 4 5 5 5 3 4 4 4 4 ...
## $ inc_3 : num [1:2651] 5 4 5 5 5 2 4 4 5 5 ...
## $ inc_4 : num [1:2651] 5 4 5 4 4 4 4 5 5 4 ...
## $ inc_5 : num [1:2651] 3 3 5 3 5 4 4 5 5 5 ...
## $ lea_1 : num [1:2651] 4 4 5 5 5 2 5 5 5 3 ...
## $ lea_2 : num [1:2651] 5 5 5 5 5 4 5 5 5 5 ...
## $ lea_3 : chr [1:2651] "5" "5" "5" "5" ...
## $ lea_4 : num [1:2651] 5 5 5 5 5 4 5 5 5 5 ...
## $ age : chr [1:2651] "35-44" "45-54" "18-24" "45-54" ...
## $ hiredate : chr [1:2651] "25/02/2024" "02/10/2022" "20/10/2023" "08/12/2018" ...
## $ race : chr [1:2651] NA NA NA "White" ...
## $ gender : chr [1:2651] "Male" "Male" "Male" "Male" ...
## $ manager_status: chr [1:2651] "Non-Manager" "Non-Manager" "Non-Manager" "Manager" ...
## $ country : chr [1:2651] "France" "France" "United Kingdom" "United States" ...library(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.1, built: 2022-11-18)
## ## Copyright (C) 2005-2024 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##missmap(df_original, main = 'missing data map for the Hooli Survey Data', col = c('purple', 'black'), legend = TRUE)
# except for race, most values are not missing!
# Creating a new df that for data preparation & processing for analysis while preserving the original df
df <- df_original
# Fixing errors in 'lea_3'
table(df_original$lea_3)##
## -1 1 2 3 4 5 99 N/A
## 12 22 58 197 1147 1213 1 1df[df$lea_3 == "N/A", ]df$lea_3[df$lea_3 == "N/A"] <- -1
table(df$lea_3)##
## -1 1 2 3 4 5 99
## 13 22 58 197 1147 1213 1#transforming 'lea_3' to a numeric variable
df$lea_3 <- as.numeric(df$lea_3)
#transforming hiredate into a date variable using different date formats for different countries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf <- df %>%
mutate(hiredate = case_when(
country %in% c("Australia", "Denmark", "France", "Germany", "India", "UK", "United Kingdom") ~ as.Date(hiredate, format = "%d/%m/%Y"),
country %in% c("United States", "USA", "Canada") ~ as.Date(hiredate, format = "%m/%d/%Y"),
country == "China" ~ as.Date(hiredate, format = "%Y/%m/%d"),
TRUE ~ as.Date(NA)
))
library(summarytools)
dfSummary(df$hiredate)## df$hiredate was converted to a data frame
2.2.1.3 Handling outliers, ‘99’, in various variables
I identified several outliers, value ‘99’, from the following variables: ali_1, ali_2, ali_3, col_1, col_2, col_3, and lea_4. The value ‘99’ is a value that is out of the scale of -1 to 5. I discovered that all these responses come from Denmark. Thus, it was probably a systematic coding error for these responses from Denmark. Given that these are psychological measures, it doesn’t make sense code 99 might denote an exceptional response out of -1 to 5 scale. Thus, I deem 99 as a coding error. Additionally, these employees are less than 1% of the participants in the data and that they completed all the other questions in the survey. Therefore, I recode 99 to -1, the code for missing value.
library(summarytools)
dfSummary(df)# examine the responses of the employees who responded '99' to one of these questions.
df[df$ali_1 == 99, ] # other responses are in the normal scale range & 2 participants = M00238 & M00568df[df$ali_2 == 99, ] # except for ali_2 & lea_4, all the other responses are in the normal scale range & 1 participant = M01455df[df$ali_3 == 99, ] # other responses are in the normal scale range & 1 participant = M02319df[df$col_1 == 99, ] # other responses are in the normal scale range & 2 participants = M01339, M01843df[df$col_2 == 99, ] # other responses are in the normal scale range & 1 participant = M00168df[df$col_3 == 99, ] # other responses are in the normal scale range & 1 participant = M01393df[df$lea_3 == 99, ] # except fo lea_3, all the other responses are in the normal scale range & 1 participant = M01484df[df$lea_4 == 99, ] # except for ali_2 & lea_4, all the other responses are in the normal scale range & 1 participant = M01455# recode values with 99 to -1.
df$ali_1[df$ali_1 == 99] <- -1
df$ali_2[df$ali_2 == 99] <- -1
df$ali_3[df$ali_3 == 99] <- -1
df$col_1[df$col_1 == 99] <- -1
df$col_2[df$col_2 == 99] <- -1
df$col_3[df$col_3 == 99] <- -1
df$lea_3[df$lea_3 == 99] <- -1
df$lea_4[df$lea_4 == 99] <- -12.2.1.4 Handling missing values
col_1, eng_2, inc_2, & inc_5 have 1 missing value each. It should be correctly coded as -1, the correct code for missing values.
df[is.na(df$col_1), ] #for employee_ID M02429, only col_1 is missing. It should be coded as -1. df[is.na(df$eng_2), ] #for employee_ID M01597, only eng_2 is missing. It should be coded as -1.df[is.na(df$inc_2), ] #for employee_ID M01481, only inc_2 is missing. It should be coded as -1.df[is.na(df$inc_5), ] #for employee_ID M01722, only inc_5 is missing. It should be coded as -1. df$col_1[is.na(df$col_1)] <- -1
df$eng_2[is.na(df$eng_2)] <- -1
df$inc_2[is.na(df$inc_2)] <- -1
df$inc_5[is.na(df$inc_5)] <- -12.2.1.5 Checking for duplicates
there are no duplicates based on employee ID. each response comes from a unique employee.
sum(duplicated(df$eeid))## [1] 02.2.1.6 fixing groups in ‘country’ variable
For the ‘country’ variable, ‘UK’ and ‘United Kingdom’ should be merged into one category. Similarly, ‘USA’ and ‘United States’ should be merged into one category. Let’s combine these categories for each country.
table(df$country)##
## Australia Canada China Denmark France
## 101 84 60 24 136
## Germany India UK United Kingdom United States
## 48 288 11 348 1520
## USA
## 31df$country[df$country == "UK"] <- "United Kingdom"
df$country[df$country == "USA"] <- "United States"
table(df$country)##
## Australia Canada China Denmark France
## 101 84 60 24 136
## Germany India United Kingdom United States
## 48 288 359 15512.2.1.7 consolidating ‘age’ variable’s categories
* the group ‘19’, ‘21’ should be consolidated into ‘18-24’. *
the group ‘26’, ‘28’ should be consolidated into ‘25-34’. * the group
‘39’, ‘42’ should be consolidated into ‘35-44’. * the group ‘48’ should
be consolidated into ‘45-54’.
table(df$age, useNA = "always")##
## 18-24 19 21 25-34 26 28 35-44 39 42 45-54 48 55-64 65+
## 147 1 1 1266 2 1 795 1 1 301 1 78 54
## N/A <NA>
## 2 0df$age[df$age == "19"] <- "18-24"
df$age[df$age == "21"] <- "18-24"
df$age[df$age == "26"] <- "25-34"
df$age[df$age == "28"] <- "25-34"
df$age[df$age == "39"] <- "35-44"
df$age[df$age == "42"] <- "35-44"
df$age[df$age == "48"] <- "45-54"2.2.1.8 correctly consolidating ‘race’ variable’s categories
For ‘race’ variable, “Black or African American” & “Black or African Americans” should be merged into one group.
table(df$race, useNA = "always")##
## American Indian/Alaskan Native
## 2
## Asian
## 359
## Black or African American
## 37
## Black or African Americans
## 11
## Hispanic or Latino
## 80
## Native Hawaiian or Other Pacific Islander
## 2
## Two or More Races
## 43
## White
## 1050
## <NA>
## 1067df$race[df$race == "Black or African Americans"] <- "Black or African American"2.2.1.9 correctly consolidating ‘gender’ variable’s categories
For ‘gender’ variable, these groups below need to be consolidated: * Consolidate “Male” & “Man” into one group * Consolidate “Female” & “Woman” into one group
table(df$gender, useNA = "always")##
## Female Male Man Unknown Woman <NA>
## 816 1826 4 1 4 0df$gender[df$gender == "Man"] <- "Male"
df$gender[df$gender == "Woman"] <- "Female"2.2.1.10 examining race variable’s missing observations
Based on the contingency table below, 40.2% of race’s variable’s
observations are missing:
- 101/101 employees in Australia have no information about their race.
- 60/84 employees in Canada have no information about their race except 24 who are white.
- 55/60 employees in China have no information about their race except 5 who are white.
- 24/24 employees in Denmark have no information about their race.
- 136/136 employees in France have no information about their race.
- 48/48 employees in Germany have no information about their race.
- 288/288 employees in India have no information about their race.
- 355/359 employees in the United Kingdom have no information about their race.
- 1017/1017 employees in the United States have demographic information about race.
Except for the United States, all the countries with
missing information about race are largely ethnically homogeneous (e.g.,
Denmark). Therefore, the company might not collect information about
employee’s race as the variance in race among employees might be too
small to be a meaningful (For example, according to demographic
research, about 90.2 percent of the population in Australia are white).
Therefore, conducting an analysis about race for other countries except
for the United States would not provide meaningful insights to
Hooli.
c_t <- table(df$race, df$country, useNA = "always")
c_t##
## Australia Canada China Denmark
## American Indian/Alaskan Native 0 0 0 0
## Asian 0 0 0 0
## Black or African American 0 0 0 0
## Hispanic or Latino 0 0 0 0
## Native Hawaiian or Other Pacific Islander 0 0 0 0
## Two or More Races 0 0 0 0
## White 0 24 5 0
## <NA> 101 60 55 24
##
## France Germany India United Kingdom
## American Indian/Alaskan Native 0 0 0 0
## Asian 0 0 0 0
## Black or African American 0 0 0 0
## Hispanic or Latino 0 0 0 0
## Native Hawaiian or Other Pacific Islander 0 0 0 0
## Two or More Races 0 0 0 0
## White 0 0 0 4
## <NA> 136 48 288 355
##
## United States <NA>
## American Indian/Alaskan Native 2 0
## Asian 359 0
## Black or African American 48 0
## Hispanic or Latino 80 0
## Native Hawaiian or Other Pacific Islander 2 0
## Two or More Races 43 0
## White 1017 0
## <NA> 0 02.2.1.11 Examining ‘hiredate’ variable.
None of the observations are missing.
df_2 <- subset(df, select = c(hiredate))
library(Amelia)
missmap(df_2)
rm(df_2)
#the earliest & latest date for hiredate
min(df$hiredate, na.rm = TRUE) # 2014-12-24## [1] "2004-12-24"max(df$hiredate, na.rm = TRUE) # 2024-02-29## [1] "2024-02-29"#let's create a new variable named hireyear
df$hireyear <- format(df$hiredate, "%Y")
table(df$hireyear, useNA = "always")##
## 2004 2005 2006 2010 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 <NA>
## 1 1 1 1 41 40 31 42 125 129 307 308 632 954 38 0library(ggplot2)
ggplot(df, aes(x=hireyear)) + geom_bar()
ggplot(df, aes(x=hiredate)) + geom_histogram() #this company hired employees a ton in 2022!!## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
2.2.1.12 creating a new variable called ‘tenure’ by using hiredate
Substract ‘hiredate’ from March 1st 2024, the date instructed by the technical assessment rubric to create different tenure groups by using ‘hiredate’.Given that there are only 4 employees in ‘10+ years’ groups, we do not report any findings about this group as the reporting minimum is 5 per group.
#let's create the cutpoint, which is 2024-03-01, to create tenure groups
cutpoint <- as.Date("2024-03-01")
#given that the average number of days in a month is 365/12 = 30.4166666667,
tenure_days <- as.numeric(difftime(cutpoint, df$hiredate, units = "days"))
tenure_months <- as.numeric(difftime(cutpoint, df$hiredate, units = "days")/(365/12))
#create tenure_groups
tenure_groups <- cut(tenure_months,
breaks = c(0, 3, 6, 12, 24, 48, 72, 120, Inf), # In months
labels = c("Under 3 months", "3-6 months", "6-12 months", "1-2 years", "2-4 years", "4-6 years", "6-10 years", "10+ years"), right = FALSE) # right = FALSE to EXCLUDE the right side month in the cateogry
#the distribution of employees in each tenure group
table(tenure_groups, useNA = "always")## tenure_groups
## Under 3 months 3-6 months 6-12 months 1-2 years 2-4 years
## 64 325 507 670 644
## 4-6 years 6-10 years 10+ years <NA>
## 274 163 4 0# Add tenure_group variable to data frame
df$tenure_group <- as.character(tenure_groups)
# Print the data frame with the new tenure_group variable
table(df$tenure_group, useNA = "always")##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years
## 670 4 644 325 274
## 6-10 years 6-12 months Under 3 months <NA>
## 163 507 64 02.2.1.13 recoding -1 (missing values) to NA
I changed all the missing values (-1) to NA (I named this new
dataset df_2) to make sure that ‘-1’ does not influence the results of
our analysis.
df_2 <- df
df_2$ali_1[df_2$ali_1 == -1] <- NA
df_2$ali_2[df_2$ali_2 == -1] <- NA
df_2$ali_3[df_2$ali_3 == -1] <- NA
df_2$col_1[df_2$col_1 == -1] <- NA
df_2$col_2[df_2$col_2 == -1] <- NA
df_2$col_3[df_2$col_3 == -1] <- NA
df_2$eng_1[df_2$eng_1 == -1] <- NA
df_2$eng_2[df_2$eng_2 == -1] <- NA
df_2$eng_3[df_2$eng_3 == -1] <- NA
df_2$eng_4[df_2$eng_4 == -1] <- NA
df_2$eng_5[df_2$eng_5 == -1] <- NA
df_2$inc_1[df_2$inc_1 == -1] <- NA
df_2$inc_2[df_2$inc_2 == -1] <- NA
df_2$inc_3[df_2$inc_3 == -1] <- NA
df_2$inc_4[df_2$inc_4 == -1] <- NA
df_2$inc_5[df_2$inc_5 == -1] <- NA
df_2$lea_1[df_2$lea_1 == -1] <- NA
df_2$lea_2[df_2$lea_2 == -1] <- NA
df_2$lea_3[df_2$lea_3 == -1] <- NA
df_2$lea_4[df_2$lea_4 == -1] <- NA
df_2$age[df_2$age == "N/A"] <- NA
#we convert "Unknown" to NA as this category has less than 5 people and we do not report.
df_2$gender[df_2$gender == "Unknown"] <- NA
dfSummary(df_2)#the end of data-processing & preparation for analysis
3 DATA ANALYSIS
3.1 Calculate the Favorable Scores for Qll Questions and Factors for Hooli Overall.
3.1.1 Calculating the favorable scores for all questions
Favorable scores are defined as the sum of the number of
answers that are agree and strongly agree over the number of all non
missing responses.
#let's create a function that automates the process of calculating favorable scores for all questions at once:
function_favorability <- function(df) {
favorability_score <- numeric(ncol(df)) # Include all columns
column_names <- names(df) # Get the column names
for (i in 1:ncol(df)) { # Start from column 1
# Calculate the proportion
favorability_score[i] <- sum(df[[i]] %in% c(4,5)) / sum(df[[i]] %in% c(1:5))
}
# Combine favorability scores and column names into a data frame
result <- data.frame(column_name = column_names, favorability_score = favorability_score)
return(result)
}
# Calculate favorability_score for columns 2 to 21
favorability_score <- function_favorability(df_2[, 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8864413
## 2 ali_2 0.8325086
## 3 ali_3 0.6844309
## 4 col_1 0.8237301
## 5 col_2 0.6013986
## 6 col_3 0.8057883
## 7 eng_1 0.9209830
## 8 eng_2 0.8507181
## 9 eng_3 0.8010610
## 10 eng_4 0.6852062
## 11 eng_5 0.7222853
## 12 inc_1 0.8958967
## 13 inc_2 0.7247278
## 14 inc_3 0.8119954
## 15 inc_4 0.6996521
## 16 inc_5 0.7749616
## 17 lea_1 0.8335234
## 18 lea_2 0.8491641
## 19 lea_3 0.8949564
## 20 lea_4 0.8773728favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
print(favorability_score_percent)## question_number favorability_score
## 1 ali_1 89
## 2 ali_2 83
## 3 ali_3 68
## 4 col_1 82
## 5 col_2 60
## 6 col_3 81
## 7 eng_1 92
## 8 eng_2 85
## 9 eng_3 80
## 10 eng_4 69
## 11 eng_5 72
## 12 inc_1 90
## 13 inc_2 72
## 14 inc_3 81
## 15 inc_4 70
## 16 inc_5 77
## 17 lea_1 83
## 18 lea_2 85
## 19 lea_3 89
## 20 lea_4 88#graph favorable score for each question
##setting different colors for different factors
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
##
group_colors <- c(
"Alignment" = "#D08770", # Light orange for Group 1
"Collaboration" = "#A3BE8C", # Light green for Group 2
"Engagement" = "#5E81AC", # Light blue for Group 3
"Inclusion" = "#EBCB8B", # Light yellow for Group 4
"Leadership" = "#B48EAD" # Mild purple for Group 5
)
## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Question Item") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
3.1.2 Calculating a ‘factor favorable score’, which is the average favorable score among questions within the same factor, for each factor
#let's create a function that automates the process of calculating of factor favorable score.
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8011269
## 2 col 0.7436390
## 3 eng 0.7960507
## 4 inc 0.7814467
## 5 lea 0.8637542#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 80 Alignment
## 1 ali_1 89 Alignment
## 2 ali_2 83 Alignment
## 3 ali_3 68 Alignment
## 22 col 74 Collaboration
## 4 col_1 82 Collaboration
## 5 col_2 60 Collaboration
## 6 col_3 81 Collaboration
## 23 eng 80 Engagement
## 7 eng_1 92 Engagement
## 8 eng_2 85 Engagement
## 9 eng_3 80 Engagement
## 10 eng_4 69 Engagement
## 11 eng_5 72 Engagement
## 24 inc 78 Inclusion
## 12 inc_1 90 Inclusion
## 13 inc_2 72 Inclusion
## 14 inc_3 81 Inclusion
## 15 inc_4 70 Inclusion
## 16 inc_5 77 Inclusion
## 25 lea 86 Leadership
## 17 lea_1 83 Leadership
## 18 lea_2 85 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 88 Leadership## Create a ggplot bar plot with the preselected colors (vertical)
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores (vertical)
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's enagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "Overall",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenure#create a graph that shows enagement favorable score across tenure groups (all)
engagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all#Desired order of question_number in reverse as I am making a horizontal graph
desired_order <- c("eng_5", "eng_4", "eng_3", "eng_2", "eng_1", "eng")
#Set the factor levels in reverse order
engagement_favorable_score_for_all$question_number <- factor(
engagement_favorable_score_for_all$question_number,
levels = (desired_order) # Reverse the desired order
)
#Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
#Color palette for highlighting
highlight_colors <- c("first" = "#9A36B2", # Purple for the 'first' category
"other" = "#C9A5DD") # light purple for the 'other' category
#Create a horizontal bar plot with ggplot
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
coord_flip() +
labs(
x = "Factor Items",
y = "Favorability Score",
title = "Engagement Factor Favorable Scores",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses. Factor favorability score is an average of all the items for the factor." # Add the caption
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
plot.caption = element_text(size = 6, hjust = 0)
) +
geom_text(aes(label = paste0(favorability_score, "%")),
hjust = -0.3,
size = 4) +
scale_fill_manual(values = highlight_colors) 
3.2 Calculate Engagement Factor (Favorable) Scores by All Tenure Groups
3.2.1 Engagement factor favorable scores for employees whose tenure are “Under 3 Months”
n = 64
# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$tenure_group == "Under 3 months", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.984375
## 2 eng_2 0.859375
## 3 eng_3 0.906250
## 4 eng_4 0.890625
## 5 eng_5 0.828125# Calculate the average engagement factor favorable score for those under 3 months
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.984375
## 2 eng_2 0.859375
## 3 eng_3 0.906250
## 4 eng_4 0.890625
## 5 eng_5 0.828125
## 6 average 0.893750favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 98
## 2 eng_2 86
## 3 eng_3 91
## 4 eng_4 89
## 5 eng_5 83
## 6 average 89#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # Purple for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with Under 3 Months Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "Under 3 Months"
3.2.2 Engagement factor favorability scores for employees whose tenure are “3-6 months”
n = 325
#3-6 months
df_2[df_2$tenure_group == "3-6 months" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "3-6 months", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9506173
## 2 eng_2 0.9040248
## 3 eng_3 0.8796296
## 4 eng_4 0.8209877
## 5 eng_5 0.8580247# Calculate the average engagement factor favorable score for those with 3-6 months tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9506173
## 2 eng_2 0.9040248
## 3 eng_3 0.8796296
## 4 eng_4 0.8209877
## 5 eng_5 0.8580247
## 6 average 0.8826568favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 95
## 2 eng_2 90
## 3 eng_3 88
## 4 eng_4 82
## 5 eng_5 86
## 6 average 88#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 3-6 months tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 3-6 Months Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "3-6 Months"
3.2.3 Engagement factor favorability scores for employees whose tenure are “6-12 months”
n = 507
#6-12 months
df_2[df_2$tenure_group == "6-12 months" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "6-12 months", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9090909
## 2 eng_2 0.8106509
## 3 eng_3 0.8134921
## 4 eng_4 0.7199211
## 5 eng_5 0.7312253# Calculate the average engagement factor favorable score for those with 6-12 months tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9090909
## 2 eng_2 0.8106509
## 3 eng_3 0.8134921
## 4 eng_4 0.7199211
## 5 eng_5 0.7312253
## 6 average 0.7968761favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 91
## 2 eng_2 81
## 3 eng_3 81
## 4 eng_4 72
## 5 eng_5 73
## 6 average 80#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 6-12 months tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 6-12 Months Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "6-12 Months"
3.2.4 Engagement factor favorability scores for employees whose tenure are “1-2 years”
n= 670
#1-2 years
df_2[df_2$tenure_group == "1-2 years" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "1-2 years", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9235382
## 2 eng_2 0.8579970
## 3 eng_3 0.7976012
## 4 eng_4 0.6616766
## 5 eng_5 0.7140719# Calculate the average engagement factor favorable score for those with 1-2 years tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9235382
## 2 eng_2 0.8579970
## 3 eng_3 0.7976012
## 4 eng_4 0.6616766
## 5 eng_5 0.7140719
## 6 average 0.7909770favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 92
## 2 eng_2 86
## 3 eng_3 80
## 4 eng_4 66
## 5 eng_5 71
## 6 average 79#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 1-2 years tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 1-2 years Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "1-2 Years"
3.2.5 Engagement factor favorability scores for employees whose tenure are “2-4 years”
n = 644
#2-4 years
df_2[df_2$tenure_group == "2-4 years" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "2-4 years", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9144635
## 2 eng_2 0.8429238
## 3 eng_3 0.7659906
## 4 eng_4 0.6209048
## 5 eng_5 0.6931464# Calculate the average engagement factor favorable score for those with 2-4 years tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9144635
## 2 eng_2 0.8429238
## 3 eng_3 0.7659906
## 4 eng_4 0.6209048
## 5 eng_5 0.6931464
## 6 average 0.7674858favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 91
## 2 eng_2 84
## 3 eng_3 77
## 4 eng_4 62
## 5 eng_5 69
## 6 average 77#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 2-4 years tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 2-4 years Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "2-4 Years"
3.2.6 Engagement factor scores for employees whose tenure are “4-6 years”
n = 274
#4-6 years
df_2[df_2$tenure_group == "4-6 years" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "4-6 years", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9014599
## 2 eng_2 0.8534799
## 3 eng_3 0.7867647
## 4 eng_4 0.6300366
## 5 eng_5 0.6167883# Calculate the average engagement factor favorable score for those with 4-6 years tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9014599
## 2 eng_2 0.8534799
## 3 eng_3 0.7867647
## 4 eng_4 0.6300366
## 5 eng_5 0.6167883
## 6 average 0.7577059favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 90
## 2 eng_2 85
## 3 eng_3 79
## 4 eng_4 63
## 5 eng_5 62
## 6 average 76# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 4-6 years tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 4-6 years Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "4-6 Years"
3.2.7 Engagement factor scores for employees whose tenure are “6-10 years”
N = 163
#6-10 years
df_2[df_2$tenure_group == "6-10 years" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "6-10 years", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9202454
## 2 eng_2 0.8588957
## 3 eng_3 0.7361963
## 4 eng_4 0.6604938
## 5 eng_5 0.7080745# Calculate the average engagement factor favorable score for those with 6-10 years tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 0.9202454
## 2 eng_2 0.8588957
## 3 eng_3 0.7361963
## 4 eng_4 0.6604938
## 5 eng_5 0.7080745
## 6 average 0.7767812favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 92
## 2 eng_2 86
## 3 eng_3 74
## 4 eng_4 66
## 5 eng_5 71
## 6 average 78# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
# graph showing engagement factor scores for the 6-10 years tenure group
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Employees with 6-10 years Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
#append this group's engagement factor favorability score to the tenure group df
engagement_by_tenure <- rbind(engagement_by_tenure, average_row)
engagement_by_tenure$question_number[engagement_by_tenure$question_number == "average"] <- "6-10 Years"
3.2.8 Engagement factor scores for employees whose tenure are “10+ years”
n = 4, given that this category has less than 5 employees, we don’t report it.
#it is critical that there are only 2 employees who have worked longer than 10 years.
#10+ years
df_2[df_2$tenure_group == "10+ years" & !is.na(df_2$tenure_group), ]# Calculate favorability_score for columns 8 to 12 (engagement questions)
favorability_score <- function_favorability(df_2[df_2$tenure_group == "10+ years", 8:12])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 1.00
## 2 eng_2 1.00
## 3 eng_3 1.00
## 4 eng_4 1.00
## 5 eng_5 0.75# Calculate the average engagement factor favorable score for those with over 10 years tenure
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
favorability_score_with_average <- rbind(favorability_score, average_row)
print(favorability_score_with_average)## question_number favorability_score
## 1 eng_1 1.00
## 2 eng_2 1.00
## 3 eng_3 1.00
## 4 eng_4 1.00
## 5 eng_5 0.75
## 6 average 0.95favorability_score_with_average_percent <- favorability_score_with_average
favorability_score_with_average_percent$favorability_score <- round(favorability_score_with_average_percent $favorability_score,2)*100
print(favorability_score_with_average_percent)## question_number favorability_score
## 1 eng_1 100
## 2 eng_2 100
## 3 eng_3 100
## 4 eng_4 100
## 5 eng_5 75
## 6 average 95#visualize the favorability scores
# Distinguish the average row from other item rows
favorability_score_with_average_percent$highlight <- ifelse(favorability_score_with_average_percent$question_number == favorability_score_with_average_percent$question_number[6], "first", "other")
ggplot(favorability_score_with_average_percent, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Favorability Scores for Employees with Over 10 Years Tenure") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5)
3.2.9 Engagement factor favorable scores across tenure groups (employee life cycle)
engagement_by_tenure$favorability_score <- round(engagement_by_tenure$favorability_score*100)
engagement_by_tenure$question_number <- factor(engagement_by_tenure$question_number, levels = engagement_by_tenure$question_number)
engagement_by_tenure# Distinguish the average row from other item rows
engagement_by_tenure$highlight <- ifelse(engagement_by_tenure$question_number == engagement_by_tenure$question_number[1], "first", "other")
# graph showing engagement factor scores across tenure groups
ggplot(engagement_by_tenure, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores Across Tenure Groups") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 11),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.2.10 Changes in employee engagement favorable scores over employee life cycle (line graphs)
library(dplyr)
library(ggplot2)
library(scales)
table(df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years
## 670 4 644 325 274
## 6-10 years 6-12 months Under 3 months
## 163 507 64#List of tenure groups
tenure_groups <- c("Under 3 months", "3-6 months", "6-12 months", "1-2 years", "2-4 years", "4-6 years", "6-10 years")
#Create an empty data frame to store the consolidated results
consolidated_favorability_scores <- data.frame(
tenure_group = character(),
question_number = character(),
favorability_score = numeric(),
stringsAsFactors = FALSE
)
#Loop through each tenure group and calculate favorability scores
for (group in tenure_groups) {
#Calculate favorability scores
favorability_score <- function_favorability(df_2[df_2$tenure_group == group, c(8:12)]) # Example column range
colnames(favorability_score)[1] <- "question_number"
#Calculate the average favorability score
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
#Create the "average" row
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
#Add the "average" row to the data frame
favorability_score <- rbind(favorability_score, average_row)
#Add the tenure group to the data frame
favorability_score <- favorability_score %>%
mutate(tenure_group = group) # Label with the tenure group
#Append to the consolidated data frame
consolidated_favorability_scores <- rbind(
consolidated_favorability_scores,
favorability_score
)
}
#set the order for tenure grouops in the new df
consolidated_favorability_scores <- consolidated_favorability_scores %>%
mutate(tenure_group = factor(tenure_group, levels = tenure_groups)) # Set the levels
print(consolidated_favorability_scores)## question_number favorability_score tenure_group
## 1 eng_1 0.9843750 Under 3 months
## 2 eng_2 0.8593750 Under 3 months
## 3 eng_3 0.9062500 Under 3 months
## 4 eng_4 0.8906250 Under 3 months
## 5 eng_5 0.8281250 Under 3 months
## 6 average 0.8937500 Under 3 months
## 7 eng_1 0.9506173 3-6 months
## 8 eng_2 0.9040248 3-6 months
## 9 eng_3 0.8796296 3-6 months
## 10 eng_4 0.8209877 3-6 months
## 11 eng_5 0.8580247 3-6 months
## 12 average 0.8826568 3-6 months
## 13 eng_1 0.9090909 6-12 months
## 14 eng_2 0.8106509 6-12 months
## 15 eng_3 0.8134921 6-12 months
## 16 eng_4 0.7199211 6-12 months
## 17 eng_5 0.7312253 6-12 months
## 18 average 0.7968761 6-12 months
## 19 eng_1 0.9235382 1-2 years
## 20 eng_2 0.8579970 1-2 years
## 21 eng_3 0.7976012 1-2 years
## 22 eng_4 0.6616766 1-2 years
## 23 eng_5 0.7140719 1-2 years
## 24 average 0.7909770 1-2 years
## 25 eng_1 0.9144635 2-4 years
## 26 eng_2 0.8429238 2-4 years
## 27 eng_3 0.7659906 2-4 years
## 28 eng_4 0.6209048 2-4 years
## 29 eng_5 0.6931464 2-4 years
## 30 average 0.7674858 2-4 years
## 31 eng_1 0.9014599 4-6 years
## 32 eng_2 0.8534799 4-6 years
## 33 eng_3 0.7867647 4-6 years
## 34 eng_4 0.6300366 4-6 years
## 35 eng_5 0.6167883 4-6 years
## 36 average 0.7577059 4-6 years
## 37 eng_1 0.9202454 6-10 years
## 38 eng_2 0.8588957 6-10 years
## 39 eng_3 0.7361963 6-10 years
## 40 eng_4 0.6604938 6-10 years
## 41 eng_5 0.7080745 6-10 years
## 42 average 0.7767812 6-10 years# line graph showing each engagement factor's change over time
ggplot(consolidated_favorability_scores, aes(x = tenure_group, y = favorability_score * 100, group = question_number, color = question_number)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorable Scores Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.",
color = "Engagement Questions"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 6, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1)) 
#create another line graph in which I emphasize average, eng_4, and eng_5
# Assign colors or line styles to emphasize specific lines
line_colors <- c(
"average" = "red", # Emphasize the "average" line with red color
"eng_4" = "blue", # Emphasize "eng_4" with blue
"eng_5" = "orange", # Emphasize "eng_5" with orange
"other" = "gray" # Use a neutral color for other lines
)
# Create a line plot with customized colors for emphasis
ggplot(consolidated_favorability_scores, aes(x = tenure_group, y = favorability_score * 100, group = question_number)) +
geom_line(aes(color = ifelse(question_number %in% c("average", "eng_4", "eng_5"), question_number, "other")), size = 1.2) + # Draw lines with custom colors
geom_point(aes(color = ifelse(question_number %in% c("average", "eng_4", "eng_5"), question_number, "other")), size = 3) + # Add points with matching colors
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorability Scores Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.", # Detailed caption
color = "Engagement Questions" # Custom legend title
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1), # Rotate x-axis labels
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 6, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1)) + # Display y-axis as percentage
scale_color_manual(values = line_colors) # Assign specific colors to lines
#Create an empty data frame to store the consolidated results for men and women separately
consolidated_favorability_scores_men <- data.frame(
tenure_group = character(),
question_number = character(),
favorability_score = numeric(),
stringsAsFactors = FALSE
)
consolidated_favorability_scores_women <- data.frame(
tenure_group = character(),
question_number = character(),
stringsAsFactors = FALSE
)
# Define tenure groups, excluding '10+ years'
tenure_groups <- c("Under 3 months", "3-6 months", "6-12 months", "1-2 years", "2-4 years", "4-6 years", "6-10 years")
# Function to calculate favorability scores and add tenure groups
calculate_scores_by_tenure <- function(data, tenure_groups) {
consolidated_scores <- data.frame(
tenure_group = character(),
question_number = character(),
favorability_score = numeric(),
stringsAsFactors = FALSE
)
for (group in tenure_groups) {
# Calculate favorability scores for each tenure group
favorability_score <- function_favorability(data[data$tenure_group == group, c(8:12)]) # Example column range
colnames(favorability_score)[1] <- "question_number"
# Calculate the average favorability score
average_score <- mean(favorability_score$favorability_score, na.rm = TRUE)
# Create the "average" row
average_row <- data.frame(
question_number = "average",
favorability_score = average_score
)
# Add the "average" row to the data frame
favorability_score <- rbind(favorability_score, average_row)
# Add the tenure group to the data frame
favorability_score <- favorability_score %>%
mutate(tenure_group = group) # Label with the tenure group
# Append to the consolidated data frame
consolidated_scores <- rbind(
consolidated_scores,
favorability_score
)
}
# Set the order for tenure groups
consolidated_scores <- consolidated_scores %>%
mutate(tenure_group = factor(tenure_group, levels = tenure_groups))
return(consolidated_scores)
}
# Calculate scores for men and women, excluding '10+ years'
consolidated_favorability_scores_men <- calculate_scores_by_tenure(df_2[df_2$gender == "Male" & df_2$tenure_group != "10+ years", ], tenure_groups)
consolidated_favorability_scores_women <- calculate_scores_by_tenure(df_2[df_2$gender == "Female" & df_2$tenure_group != "10+ years", ], tenure_groups)
# Now create the line plots for men and women, excluding '10+ years'
# Line plot for men
ggplot(consolidated_favorability_scores_men, aes(x = tenure_group, y = favorability_score * 100, group = question_number, color = question_number)) +
geom_line(size = 1.2) + # Draw lines
geom_point(size = 3) + # Add points
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorability Scores for Men Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.", # Detailed caption
color = "Engagement Questions" # Custom legend title
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1)) # Display y-axis as percentage
# Line plot for men
ggplot(consolidated_favorability_scores_men, aes(x = tenure_group, y = favorability_score * 100, group = question_number, color = question_number)) +
geom_line(size = 1.2) + # Draw lines
geom_point(size = 3) + # Add points
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorability Scores for Men Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.", # Detailed caption
color = "Engagement Questions" # Custom legend title
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1)) + # Display y-axis as percentage
scale_color_manual(values = line_colors)
# Line plot for women
ggplot(consolidated_favorability_scores_women, aes(x = tenure_group, y = favorability_score * 100, group = question_number, color = question_number)) +
geom_line(size = 1.2) + # Draw lines
geom_point(size = 3) + # Add points
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorability Scores for Women Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.", # Detailed caption
color = "Engagement Questions" # Custom legend title
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1))
# Line plot for women
ggplot(consolidated_favorability_scores_women, aes(x = tenure_group, y = favorability_score * 100, group = question_number, color = question_number)) +
geom_line(size = 1.2) + # Draw lines
geom_point(size = 3) + # Add points
labs(
x = "Tenure Group",
y = "Favorability Score (%)",
title = "Change in Engagement Favorability Scores for Women Across Employee Lifecycle",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses.", # Detailed caption
color = "Engagement Questions" # Custom legend title
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 8, hjust = 0)
) +
scale_y_continuous(labels = percent_format(scale = 1)) + # Display y-axis as percentage
scale_color_manual(values = line_colors)
3.3 Calculate Favorable Scores for All Questions and Factors by gender
3.3.1 Favorable scores for all questions and factors for women
N for female = 820
table(df_2$gender)##
## Female Male
## 820 1830df_2[df_2$gender== "Female" & !is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$gender == "Female", 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8441718
## 2 ali_2 0.7980296
## 3 ali_3 0.6584464
## 4 col_1 0.7610294
## 5 col_2 0.6022727
## 6 col_3 0.8204182
## 7 eng_1 0.8974359
## 8 eng_2 0.8095238
## 9 eng_3 0.7533742
## 10 eng_4 0.6328029
## 11 eng_5 0.6813725
## 12 inc_1 0.8620269
## 13 inc_2 0.6492537
## 14 inc_3 0.7382134
## 15 inc_4 0.6509317
## 16 inc_5 0.7719950
## 17 lea_1 0.8019680
## 18 lea_2 0.8009828
## 19 lea_3 0.8946078
## 20 lea_4 0.8378378favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 84 Alignment
## 2 ali_2 80 Alignment
## 3 ali_3 66 Alignment
## 4 col_1 76 Collaboration
## 5 col_2 60 Collaboration
## 6 col_3 82 Collaboration
## 7 eng_1 90 Engagement
## 8 eng_2 81 Engagement
## 9 eng_3 75 Engagement
## 10 eng_4 63 Engagement
## 11 eng_5 68 Engagement
## 12 inc_1 86 Inclusion
## 13 inc_2 65 Inclusion
## 14 inc_3 74 Inclusion
## 15 inc_4 65 Inclusion
## 16 inc_5 77 Inclusion
## 17 lea_1 80 Leadership
## 18 lea_2 80 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 84 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7668826
## 2 col 0.7279068
## 3 eng 0.7549019
## 4 inc 0.7344841
## 5 lea 0.8338491#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 77 Alignment
## 1 ali_1 84 Alignment
## 2 ali_2 80 Alignment
## 3 ali_3 66 Alignment
## 22 col 73 Collaboration
## 4 col_1 76 Collaboration
## 5 col_2 60 Collaboration
## 6 col_3 82 Collaboration
## 23 eng 75 Engagement
## 7 eng_1 90 Engagement
## 8 eng_2 81 Engagement
## 9 eng_3 75 Engagement
## 10 eng_4 63 Engagement
## 11 eng_5 68 Engagement
## 24 inc 73 Inclusion
## 12 inc_1 86 Inclusion
## 13 inc_2 65 Inclusion
## 14 inc_3 74 Inclusion
## 15 inc_4 65 Inclusion
## 16 inc_5 77 Inclusion
## 25 lea 83 Leadership
## 17 lea_1 80 Leadership
## 18 lea_2 80 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 84 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "Overall",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenure#create a graph that shows engagement favorable score across tenure groups (all)
engagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
#Create a ggplot that shows engagement factor favorable scores for women
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.2 Favorable scores for all questions and factors for men
n = 1830
# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$gender == "Male", 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.9053385
## 2 ali_2 0.8484013
## 3 ali_3 0.6958678
## 4 col_1 0.8517298
## 5 col_2 0.6007861
## 6 col_3 0.7991170
## 7 eng_1 0.9315068
## 8 eng_2 0.8691128
## 9 eng_3 0.8222710
## 10 eng_4 0.7090411
## 11 eng_5 0.7404162
## 12 inc_1 0.9111479
## 13 inc_2 0.7589134
## 14 inc_3 0.8450390
## 15 inc_4 0.7216611
## 16 inc_5 0.7761693
## 17 lea_1 0.8475509
## 18 lea_2 0.8706659
## 19 lea_3 0.8950549
## 20 lea_4 0.8949973favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
print(favorability_score_percent)## question_number favorability_score
## 1 ali_1 91
## 2 ali_2 85
## 3 ali_3 70
## 4 col_1 85
## 5 col_2 60
## 6 col_3 80
## 7 eng_1 93
## 8 eng_2 87
## 9 eng_3 82
## 10 eng_4 71
## 11 eng_5 74
## 12 inc_1 91
## 13 inc_2 76
## 14 inc_3 85
## 15 inc_4 72
## 16 inc_5 78
## 17 lea_1 85
## 18 lea_2 87
## 19 lea_3 90
## 20 lea_4 89library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 91 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 70 Alignment
## 4 col_1 85 Collaboration
## 5 col_2 60 Collaboration
## 6 col_3 80 Collaboration
## 7 eng_1 93 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 82 Engagement
## 10 eng_4 71 Engagement
## 11 eng_5 74 Engagement
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 85 Inclusion
## 15 inc_4 72 Inclusion
## 16 inc_5 78 Inclusion
## 17 lea_1 85 Leadership
## 18 lea_2 87 Leadership
## 19 lea_3 90 Leadership
## 20 lea_4 89 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8165359
## 2 col 0.7505443
## 3 eng 0.8144696
## 4 inc 0.8025861
## 5 lea 0.8770673#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 82 Alignment
## 1 ali_1 91 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 70 Alignment
## 22 col 75 Collaboration
## 4 col_1 85 Collaboration
## 5 col_2 60 Collaboration
## 6 col_3 80 Collaboration
## 23 eng 81 Engagement
## 7 eng_1 93 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 82 Engagement
## 10 eng_4 71 Engagement
## 11 eng_5 74 Engagement
## 24 inc 80 Inclusion
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 85 Inclusion
## 15 inc_4 72 Inclusion
## 16 inc_5 78 Inclusion
## 25 lea 88 Leadership
## 17 lea_1 85 Leadership
## 18 lea_2 87 Leadership
## 19 lea_3 90 Leadership
## 20 lea_4 89 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "Overall",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenure#create a graph that shows engagement favorable score across tenure groups (all)
engagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
# create a graph that shows engagement factor favorable scores for men
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.3 a graph comparing enagement factor favorable scores between men and women
library(ggplot2)
library(dplyr)
# Calculate engagement favorability scores for all employees
all_employees_favorability_score <- function_favorability(df_2[, 8:12])
all_employees_favorability_score$gender <- "All Employees" # Label as 'All Employees'
# Calculate engagement favorability scores for men
men_df <- df_2[df_2$gender == "Male", ]
men_favorability_score <- function_favorability(men_df[, 8:12])
men_favorability_score$gender <- "Men" # Label as 'Men'
# Calculate engagement favorability scores for women
women_df <- df_2[df_2$gender == "Female", ]
women_favorability_score <- function_favorability(women_df[, 8:12])
women_favorability_score$gender = "Women" # Label as 'Women'
# Combine all the scores into a single data frame
combined_favorability_scores <- rbind(
all_employees_favorability_score,
men_favorability_score,
women_favorability_score
)
# Convert favorability scores to percentages
combined_favorability_scores$favorability_score <- combined_favorability_scores$favorability_score * 100
# Rename the first column to "question_number"
colnames(combined_favorability_scores)[1] = "question_number"
# Create a bar graph with ggplot2
ggplot(combined_favorability_scores, aes(x = question_number, y = favorability_score, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
labs(
x = "Engagement Question",
y = "Favorability Score (%)",
title = "Engagement Favorability Scores by Gender",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all responses."
)+
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 1), # Rotate x-axis labels
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 6, hjust = 0) # Style the title
) +
geom_text(aes(label = paste0(round(favorability_score, 1), "%")), # Add percentage labels
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3.5) + # Adjust text size
scale_fill_manual( # Custom colors for gender
values = c("Men" = "skyblue", "Women" = "purple", "All Employees" = "gray")
)
3.3.4 Favorable scores for all questions for women for Under 3 months
n = 25
df_2[
df_2$gender == "Female" &
df_2$tenure_group == "Under 3 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "Under 3 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.6000000
## 2 ali_2 0.8800000
## 3 ali_3 0.6800000
## 4 col_1 0.8800000
## 5 col_2 0.6500000
## 6 col_3 0.7916667
## 7 eng_1 0.9600000
## 8 eng_2 0.8000000
## 9 eng_3 0.9200000
## 10 eng_4 0.9600000
## 11 eng_5 0.8000000
## 12 inc_1 0.9200000
## 13 inc_2 0.7826087
## 14 inc_3 0.9200000
## 15 inc_4 0.7600000
## 16 inc_5 0.9200000
## 17 lea_1 0.9600000
## 18 lea_2 0.9200000
## 19 lea_3 0.9600000
## 20 lea_4 0.9600000favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 60 Alignment
## 2 ali_2 88 Alignment
## 3 ali_3 68 Alignment
## 4 col_1 88 Collaboration
## 5 col_2 65 Collaboration
## 6 col_3 79 Collaboration
## 7 eng_1 96 Engagement
## 8 eng_2 80 Engagement
## 9 eng_3 92 Engagement
## 10 eng_4 96 Engagement
## 11 eng_5 80 Engagement
## 12 inc_1 92 Inclusion
## 13 inc_2 78 Inclusion
## 14 inc_3 92 Inclusion
## 15 inc_4 76 Inclusion
## 16 inc_5 92 Inclusion
## 17 lea_1 96 Leadership
## 18 lea_2 92 Leadership
## 19 lea_3 96 Leadership
## 20 lea_4 96 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7200000
## 2 col 0.7738889
## 3 eng 0.8880000
## 4 inc 0.8605217
## 5 lea 0.9500000#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 72 Alignment
## 1 ali_1 60 Alignment
## 2 ali_2 88 Alignment
## 3 ali_3 68 Alignment
## 22 col 77 Collaboration
## 4 col_1 88 Collaboration
## 5 col_2 65 Collaboration
## 6 col_3 79 Collaboration
## 23 eng 89 Engagement
## 7 eng_1 96 Engagement
## 8 eng_2 80 Engagement
## 9 eng_3 92 Engagement
## 10 eng_4 96 Engagement
## 11 eng_5 80 Engagement
## 24 inc 86 Inclusion
## 12 inc_1 92 Inclusion
## 13 inc_2 78 Inclusion
## 14 inc_3 92 Inclusion
## 15 inc_4 76 Inclusion
## 16 inc_5 92 Inclusion
## 25 lea 95 Leadership
## 17 lea_1 96 Leadership
## 18 lea_2 92 Leadership
## 19 lea_3 96 Leadership
## 20 lea_4 96 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "Under 3 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenure#create a graph that shows engagement favorable scores for women with tenure under 3 months
engagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure Under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.5 Favorable scores for all questions for women with tenure between 3-6 months
n = 116
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "3-6 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "3-6 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.9130435
## 2 ali_2 0.8782609
## 3 ali_3 0.7739130
## 4 col_1 0.8534483
## 5 col_2 0.5904762
## 6 col_3 0.9130435
## 7 eng_1 0.9396552
## 8 eng_2 0.9130435
## 9 eng_3 0.8448276
## 10 eng_4 0.7931034
## 11 eng_5 0.8448276
## 12 inc_1 0.8782609
## 13 inc_2 0.6725664
## 14 inc_3 0.7894737
## 15 inc_4 0.7433628
## 16 inc_5 0.7982456
## 17 lea_1 0.8347826
## 18 lea_2 0.8956522
## 19 lea_3 0.9391304
## 20 lea_4 0.8608696favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 91 Alignment
## 2 ali_2 88 Alignment
## 3 ali_3 77 Alignment
## 4 col_1 85 Collaboration
## 5 col_2 59 Collaboration
## 6 col_3 91 Collaboration
## 7 eng_1 94 Engagement
## 8 eng_2 91 Engagement
## 9 eng_3 84 Engagement
## 10 eng_4 79 Engagement
## 11 eng_5 84 Engagement
## 12 inc_1 88 Inclusion
## 13 inc_2 67 Inclusion
## 14 inc_3 79 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 80 Inclusion
## 17 lea_1 83 Leadership
## 18 lea_2 90 Leadership
## 19 lea_3 94 Leadership
## 20 lea_4 86 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8550725
## 2 col 0.7856560
## 3 eng 0.8670915
## 4 inc 0.7763819
## 5 lea 0.8826087#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 86 Alignment
## 1 ali_1 91 Alignment
## 2 ali_2 88 Alignment
## 3 ali_3 77 Alignment
## 22 col 79 Collaboration
## 4 col_1 85 Collaboration
## 5 col_2 59 Collaboration
## 6 col_3 91 Collaboration
## 23 eng 87 Engagement
## 7 eng_1 94 Engagement
## 8 eng_2 91 Engagement
## 9 eng_3 84 Engagement
## 10 eng_4 79 Engagement
## 11 eng_5 84 Engagement
## 24 inc 78 Inclusion
## 12 inc_1 88 Inclusion
## 13 inc_2 67 Inclusion
## 14 inc_3 79 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 80 Inclusion
## 25 lea 88 Leadership
## 17 lea_1 83 Leadership
## 18 lea_2 90 Leadership
## 19 lea_3 94 Leadership
## 20 lea_4 86 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 3-6 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 3-6 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "3-6 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 3-6 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.6 Favorable scores for all questions for women with tenure between 6-12 months
n = 181
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "6-12 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "6-12 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8166667
## 2 ali_2 0.7777778
## 3 ali_3 0.7111111
## 4 col_1 0.7262570
## 5 col_2 0.5674157
## 6 col_3 0.7734807
## 7 eng_1 0.8950276
## 8 eng_2 0.7734807
## 9 eng_3 0.7932961
## 10 eng_4 0.7071823
## 11 eng_5 0.6944444
## 12 inc_1 0.8618785
## 13 inc_2 0.6944444
## 14 inc_3 0.8100559
## 15 inc_4 0.6815642
## 16 inc_5 0.7888889
## 17 lea_1 0.7944444
## 18 lea_2 0.8333333
## 19 lea_3 0.8674033
## 20 lea_4 0.8287293favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 82 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 71 Alignment
## 4 col_1 73 Collaboration
## 5 col_2 57 Collaboration
## 6 col_3 77 Collaboration
## 7 eng_1 90 Engagement
## 8 eng_2 77 Engagement
## 9 eng_3 79 Engagement
## 10 eng_4 71 Engagement
## 11 eng_5 69 Engagement
## 12 inc_1 86 Inclusion
## 13 inc_2 69 Inclusion
## 14 inc_3 81 Inclusion
## 15 inc_4 68 Inclusion
## 16 inc_5 79 Inclusion
## 17 lea_1 79 Leadership
## 18 lea_2 83 Leadership
## 19 lea_3 87 Leadership
## 20 lea_4 83 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7685185
## 2 col 0.6890511
## 3 eng 0.7726862
## 4 inc 0.7673664
## 5 lea 0.8309776#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 77 Alignment
## 1 ali_1 82 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 71 Alignment
## 22 col 69 Collaboration
## 4 col_1 73 Collaboration
## 5 col_2 57 Collaboration
## 6 col_3 77 Collaboration
## 23 eng 77 Engagement
## 7 eng_1 90 Engagement
## 8 eng_2 77 Engagement
## 9 eng_3 79 Engagement
## 10 eng_4 71 Engagement
## 11 eng_5 69 Engagement
## 24 inc 77 Inclusion
## 12 inc_1 86 Inclusion
## 13 inc_2 69 Inclusion
## 14 inc_3 81 Inclusion
## 15 inc_4 68 Inclusion
## 16 inc_5 79 Inclusion
## 25 lea 83 Leadership
## 17 lea_1 79 Leadership
## 18 lea_2 83 Leadership
## 19 lea_3 87 Leadership
## 20 lea_4 83 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 6-12 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 6-12 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "6-12 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 6 and 12 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.7 Favorable scores for all questions for women with tenure between 1-2 years
n = 211
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "1-2 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "1-2 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8666667
## 2 ali_2 0.7809524
## 3 ali_3 0.6507177
## 4 col_1 0.7772512
## 5 col_2 0.6201923
## 6 col_3 0.8373206
## 7 eng_1 0.8904762
## 8 eng_2 0.8293839
## 9 eng_3 0.7320574
## 10 eng_4 0.5971564
## 11 eng_5 0.6952381
## 12 inc_1 0.9004739
## 13 inc_2 0.6666667
## 14 inc_3 0.7464115
## 15 inc_4 0.6411483
## 16 inc_5 0.8038278
## 17 lea_1 0.8173077
## 18 lea_2 0.8104265
## 19 lea_3 0.9238095
## 20 lea_4 0.8619048favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 87 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 65 Alignment
## 4 col_1 78 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 84 Collaboration
## 7 eng_1 89 Engagement
## 8 eng_2 83 Engagement
## 9 eng_3 73 Engagement
## 10 eng_4 60 Engagement
## 11 eng_5 70 Engagement
## 12 inc_1 90 Inclusion
## 13 inc_2 67 Inclusion
## 14 inc_3 75 Inclusion
## 15 inc_4 64 Inclusion
## 16 inc_5 80 Inclusion
## 17 lea_1 82 Leadership
## 18 lea_2 81 Leadership
## 19 lea_3 92 Leadership
## 20 lea_4 86 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7661123
## 2 col 0.7449214
## 3 eng 0.7488624
## 4 inc 0.7517056
## 5 lea 0.8533621#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 77 Alignment
## 1 ali_1 87 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 65 Alignment
## 22 col 74 Collaboration
## 4 col_1 78 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 84 Collaboration
## 23 eng 75 Engagement
## 7 eng_1 89 Engagement
## 8 eng_2 83 Engagement
## 9 eng_3 73 Engagement
## 10 eng_4 60 Engagement
## 11 eng_5 70 Engagement
## 24 inc 75 Inclusion
## 12 inc_1 90 Inclusion
## 13 inc_2 67 Inclusion
## 14 inc_3 75 Inclusion
## 15 inc_4 64 Inclusion
## 16 inc_5 80 Inclusion
## 25 lea 85 Leadership
## 17 lea_1 82 Leadership
## 18 lea_2 81 Leadership
## 19 lea_3 92 Leadership
## 20 lea_4 86 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 1-2 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 1-2 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "1-2 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 1-2 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.8 Favorable scores for all questions for women with tenure between 2-4 years
n = 191
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "2-4 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "2-4 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8473684
## 2 ali_2 0.7765957
## 3 ali_3 0.6137566
## 4 col_1 0.7421053
## 5 col_2 0.6170213
## 6 col_3 0.8315789
## 7 eng_1 0.8900524
## 8 eng_2 0.7748691
## 9 eng_3 0.6910995
## 10 eng_4 0.5238095
## 11 eng_5 0.6000000
## 12 inc_1 0.8167539
## 13 inc_2 0.5828877
## 14 inc_3 0.6256684
## 15 inc_4 0.6276596
## 16 inc_5 0.7393617
## 17 lea_1 0.7801047
## 18 lea_2 0.7315789
## 19 lea_3 0.8691099
## 20 lea_4 0.8115183favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 85 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 61 Alignment
## 4 col_1 74 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 83 Collaboration
## 7 eng_1 89 Engagement
## 8 eng_2 77 Engagement
## 9 eng_3 69 Engagement
## 10 eng_4 52 Engagement
## 11 eng_5 60 Engagement
## 12 inc_1 82 Inclusion
## 13 inc_2 58 Inclusion
## 14 inc_3 63 Inclusion
## 15 inc_4 63 Inclusion
## 16 inc_5 74 Inclusion
## 17 lea_1 78 Leadership
## 18 lea_2 73 Leadership
## 19 lea_3 87 Leadership
## 20 lea_4 81 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7459069
## 2 col 0.7302352
## 3 eng 0.6959661
## 4 inc 0.6784663
## 5 lea 0.7980780#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 75 Alignment
## 1 ali_1 85 Alignment
## 2 ali_2 78 Alignment
## 3 ali_3 61 Alignment
## 22 col 73 Collaboration
## 4 col_1 74 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 83 Collaboration
## 23 eng 70 Engagement
## 7 eng_1 89 Engagement
## 8 eng_2 77 Engagement
## 9 eng_3 69 Engagement
## 10 eng_4 52 Engagement
## 11 eng_5 60 Engagement
## 24 inc 68 Inclusion
## 12 inc_1 82 Inclusion
## 13 inc_2 58 Inclusion
## 14 inc_3 63 Inclusion
## 15 inc_4 63 Inclusion
## 16 inc_5 74 Inclusion
## 25 lea 80 Leadership
## 17 lea_1 78 Leadership
## 18 lea_2 73 Leadership
## 19 lea_3 87 Leadership
## 20 lea_4 81 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 2-4 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 2-4 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "2-4 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 2-4 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.9 Favorable scores for all questions for women with tenure between 4-6 years
n = 58
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "4-6 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "4-6 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8448276
## 2 ali_2 0.7894737
## 3 ali_3 0.5357143
## 4 col_1 0.6315789
## 5 col_2 0.6140351
## 6 col_3 0.7142857
## 7 eng_1 0.8620690
## 8 eng_2 0.7931034
## 9 eng_3 0.7368421
## 10 eng_4 0.4655172
## 11 eng_5 0.5000000
## 12 inc_1 0.7931034
## 13 inc_2 0.5964912
## 14 inc_3 0.6607143
## 15 inc_4 0.5818182
## 16 inc_5 0.6428571
## 17 lea_1 0.7321429
## 18 lea_2 0.6607143
## 19 lea_3 0.9107143
## 20 lea_4 0.8181818favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 84 Alignment
## 2 ali_2 79 Alignment
## 3 ali_3 54 Alignment
## 4 col_1 63 Collaboration
## 5 col_2 61 Collaboration
## 6 col_3 71 Collaboration
## 7 eng_1 86 Engagement
## 8 eng_2 79 Engagement
## 9 eng_3 74 Engagement
## 10 eng_4 47 Engagement
## 11 eng_5 50 Engagement
## 12 inc_1 79 Inclusion
## 13 inc_2 60 Inclusion
## 14 inc_3 66 Inclusion
## 15 inc_4 58 Inclusion
## 16 inc_5 64 Inclusion
## 17 lea_1 73 Leadership
## 18 lea_2 66 Leadership
## 19 lea_3 91 Leadership
## 20 lea_4 82 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.7233385
## 2 col 0.6532999
## 3 eng 0.6715064
## 4 inc 0.6549969
## 5 lea 0.7804383#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 72 Alignment
## 1 ali_1 84 Alignment
## 2 ali_2 79 Alignment
## 3 ali_3 54 Alignment
## 22 col 65 Collaboration
## 4 col_1 63 Collaboration
## 5 col_2 61 Collaboration
## 6 col_3 71 Collaboration
## 23 eng 67 Engagement
## 7 eng_1 86 Engagement
## 8 eng_2 79 Engagement
## 9 eng_3 74 Engagement
## 10 eng_4 47 Engagement
## 11 eng_5 50 Engagement
## 24 inc 65 Inclusion
## 12 inc_1 79 Inclusion
## 13 inc_2 60 Inclusion
## 14 inc_3 66 Inclusion
## 15 inc_4 58 Inclusion
## 16 inc_5 64 Inclusion
## 25 lea 78 Leadership
## 17 lea_1 73 Leadership
## 18 lea_2 66 Leadership
## 19 lea_3 91 Leadership
## 20 lea_4 82 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 4-6 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 4-6 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "4-6 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 4-6 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.10 Favorable scores for all questions for women with tenure between 6-10 years
n = 37
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Female" &
df_2$tenure_group == "6-10 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Female" &
df_2$tenure_group == "6-10 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.7777778
## 2 ali_2 0.8055556
## 3 ali_3 0.4722222
## 4 col_1 0.7567568
## 5 col_2 0.5714286
## 6 col_3 0.7837838
## 7 eng_1 0.8648649
## 8 eng_2 0.7567568
## 9 eng_3 0.6216216
## 10 eng_4 0.5555556
## 11 eng_5 0.6666667
## 12 inc_1 0.9189189
## 13 inc_2 0.6111111
## 14 inc_3 0.7714286
## 15 inc_4 0.4285714
## 16 inc_5 0.6764706
## 17 lea_1 0.7567568
## 18 lea_2 0.7777778
## 19 lea_3 0.7837838
## 20 lea_4 0.7500000favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 78 Alignment
## 2 ali_2 81 Alignment
## 3 ali_3 47 Alignment
## 4 col_1 76 Collaboration
## 5 col_2 57 Collaboration
## 6 col_3 78 Collaboration
## 7 eng_1 86 Engagement
## 8 eng_2 76 Engagement
## 9 eng_3 62 Engagement
## 10 eng_4 56 Engagement
## 11 eng_5 67 Engagement
## 12 inc_1 92 Inclusion
## 13 inc_2 61 Inclusion
## 14 inc_3 77 Inclusion
## 15 inc_4 43 Inclusion
## 16 inc_5 68 Inclusion
## 17 lea_1 76 Leadership
## 18 lea_2 78 Leadership
## 19 lea_3 78 Leadership
## 20 lea_4 75 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.6851852
## 2 col 0.7039897
## 3 eng 0.6930931
## 4 inc 0.6813001
## 5 lea 0.7670796#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 69 Alignment
## 1 ali_1 78 Alignment
## 2 ali_2 81 Alignment
## 3 ali_3 47 Alignment
## 22 col 70 Collaboration
## 4 col_1 76 Collaboration
## 5 col_2 57 Collaboration
## 6 col_3 78 Collaboration
## 23 eng 69 Engagement
## 7 eng_1 86 Engagement
## 8 eng_2 76 Engagement
## 9 eng_3 62 Engagement
## 10 eng_4 56 Engagement
## 11 eng_5 67 Engagement
## 24 inc 68 Inclusion
## 12 inc_1 92 Inclusion
## 13 inc_2 61 Inclusion
## 14 inc_3 77 Inclusion
## 15 inc_4 43 Inclusion
## 16 inc_5 68 Inclusion
## 25 lea 77 Leadership
## 17 lea_1 76 Leadership
## 18 lea_2 78 Leadership
## 19 lea_3 78 Leadership
## 20 lea_4 75 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 6-10 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Women with Tenure between 6-10 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "6-10 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Women with Tenure between 6-10 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.11 Favorable scores for all questions for men for Under 3 months
n = 39
df_2[
df_2$gender == "Male" &
df_2$tenure_group == "Under 3 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "Under 3 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8461538
## 2 ali_2 0.9487179
## 3 ali_3 0.7948718
## 4 col_1 0.8717949
## 5 col_2 0.5142857
## 6 col_3 0.8717949
## 7 eng_1 1.0000000
## 8 eng_2 0.8974359
## 9 eng_3 0.8974359
## 10 eng_4 0.8461538
## 11 eng_5 0.8461538
## 12 inc_1 0.9487179
## 13 inc_2 0.7222222
## 14 inc_3 0.9210526
## 15 inc_4 0.7777778
## 16 inc_5 0.8974359
## 17 lea_1 0.7948718
## 18 lea_2 0.8974359
## 19 lea_3 0.9230769
## 20 lea_4 0.8205128favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 85 Alignment
## 2 ali_2 95 Alignment
## 3 ali_3 79 Alignment
## 4 col_1 87 Collaboration
## 5 col_2 51 Collaboration
## 6 col_3 87 Collaboration
## 7 eng_1 100 Engagement
## 8 eng_2 90 Engagement
## 9 eng_3 90 Engagement
## 10 eng_4 85 Engagement
## 11 eng_5 85 Engagement
## 12 inc_1 95 Inclusion
## 13 inc_2 72 Inclusion
## 14 inc_3 92 Inclusion
## 15 inc_4 78 Inclusion
## 16 inc_5 90 Inclusion
## 17 lea_1 79 Leadership
## 18 lea_2 90 Leadership
## 19 lea_3 92 Leadership
## 20 lea_4 82 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8632479
## 2 col 0.7526252
## 3 eng 0.8974359
## 4 inc 0.8534413
## 5 lea 0.8589744#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 86 Alignment
## 1 ali_1 85 Alignment
## 2 ali_2 95 Alignment
## 3 ali_3 79 Alignment
## 22 col 75 Collaboration
## 4 col_1 87 Collaboration
## 5 col_2 51 Collaboration
## 6 col_3 87 Collaboration
## 23 eng 90 Engagement
## 7 eng_1 100 Engagement
## 8 eng_2 90 Engagement
## 9 eng_3 90 Engagement
## 10 eng_4 85 Engagement
## 11 eng_5 85 Engagement
## 24 inc 85 Inclusion
## 12 inc_1 95 Inclusion
## 13 inc_2 72 Inclusion
## 14 inc_3 92 Inclusion
## 15 inc_4 78 Inclusion
## 16 inc_5 90 Inclusion
## 25 lea 86 Leadership
## 17 lea_1 79 Leadership
## 18 lea_2 90 Leadership
## 19 lea_3 92 Leadership
## 20 lea_4 82 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "Under 3 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenure#create a graph that shows engagement favorable score across tenure groups
engagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure Under 3 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.12 Favorable scores for all questions for men with tenure between 3-6 months
n = 209
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "3-6 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "3-6 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.9320388
## 2 ali_2 0.8888889
## 3 ali_3 0.7584541
## 4 col_1 0.9033816
## 5 col_2 0.5322581
## 6 col_3 0.8689320
## 7 eng_1 0.9567308
## 8 eng_2 0.8990385
## 9 eng_3 0.8990385
## 10 eng_4 0.8365385
## 11 eng_5 0.8653846
## 12 inc_1 0.9268293
## 13 inc_2 0.7587940
## 14 inc_3 0.8712871
## 15 inc_4 0.7450000
## 16 inc_5 0.7980296
## 17 lea_1 0.8743961
## 18 lea_2 0.9077670
## 19 lea_3 0.9275362
## 20 lea_4 0.9275362favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 93 Alignment
## 2 ali_2 89 Alignment
## 3 ali_3 76 Alignment
## 4 col_1 90 Collaboration
## 5 col_2 53 Collaboration
## 6 col_3 87 Collaboration
## 7 eng_1 96 Engagement
## 8 eng_2 90 Engagement
## 9 eng_3 90 Engagement
## 10 eng_4 84 Engagement
## 11 eng_5 87 Engagement
## 12 inc_1 93 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 87 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 80 Inclusion
## 17 lea_1 87 Leadership
## 18 lea_2 91 Leadership
## 19 lea_3 93 Leadership
## 20 lea_4 93 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8597939
## 2 col 0.7681906
## 3 eng 0.8913462
## 4 inc 0.8199880
## 5 lea 0.9093089#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 86 Alignment
## 1 ali_1 93 Alignment
## 2 ali_2 89 Alignment
## 3 ali_3 76 Alignment
## 22 col 77 Collaboration
## 4 col_1 90 Collaboration
## 5 col_2 53 Collaboration
## 6 col_3 87 Collaboration
## 23 eng 89 Engagement
## 7 eng_1 96 Engagement
## 8 eng_2 90 Engagement
## 9 eng_3 90 Engagement
## 10 eng_4 84 Engagement
## 11 eng_5 87 Engagement
## 24 inc 82 Inclusion
## 12 inc_1 93 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 87 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 80 Inclusion
## 25 lea 91 Leadership
## 17 lea_1 87 Leadership
## 18 lea_2 91 Leadership
## 19 lea_3 93 Leadership
## 20 lea_4 93 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 3-6 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 3-6 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "3-6 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 3-6 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.13 Favorable scores for all questions for men with tenure between 6-12 months
n = 325
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "6-12 months" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "6-12 months" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8981481
## 2 ali_2 0.8452012
## 3 ali_3 0.6677019
## 4 col_1 0.8664596
## 5 col_2 0.5460317
## 6 col_3 0.8204334
## 7 eng_1 0.9166667
## 8 eng_2 0.8307692
## 9 eng_3 0.8240741
## 10 eng_4 0.7292308
## 11 eng_5 0.7507692
## 12 inc_1 0.9068323
## 13 inc_2 0.7746032
## 14 inc_3 0.8593750
## 15 inc_4 0.6813880
## 16 inc_5 0.7875000
## 17 lea_1 0.8456790
## 18 lea_2 0.8641975
## 19 lea_3 0.9138462
## 20 lea_4 0.8947368favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 90 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 67 Alignment
## 4 col_1 87 Collaboration
## 5 col_2 55 Collaboration
## 6 col_3 82 Collaboration
## 7 eng_1 92 Engagement
## 8 eng_2 83 Engagement
## 9 eng_3 82 Engagement
## 10 eng_4 73 Engagement
## 11 eng_5 75 Engagement
## 12 inc_1 91 Inclusion
## 13 inc_2 77 Inclusion
## 14 inc_3 86 Inclusion
## 15 inc_4 68 Inclusion
## 16 inc_5 79 Inclusion
## 17 lea_1 85 Leadership
## 18 lea_2 86 Leadership
## 19 lea_3 91 Leadership
## 20 lea_4 89 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8036837
## 2 col 0.7443083
## 3 eng 0.8103020
## 4 inc 0.8019397
## 5 lea 0.8796149#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 80 Alignment
## 1 ali_1 90 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 67 Alignment
## 22 col 74 Collaboration
## 4 col_1 87 Collaboration
## 5 col_2 55 Collaboration
## 6 col_3 82 Collaboration
## 23 eng 81 Engagement
## 7 eng_1 92 Engagement
## 8 eng_2 83 Engagement
## 9 eng_3 82 Engagement
## 10 eng_4 73 Engagement
## 11 eng_5 75 Engagement
## 24 inc 80 Inclusion
## 12 inc_1 91 Inclusion
## 13 inc_2 77 Inclusion
## 14 inc_3 86 Inclusion
## 15 inc_4 68 Inclusion
## 16 inc_5 79 Inclusion
## 25 lea 88 Leadership
## 17 lea_1 85 Leadership
## 18 lea_2 86 Leadership
## 19 lea_3 91 Leadership
## 20 lea_4 89 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 6-12 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 6-12 months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "6-12 months",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 6 and 12 Months") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.14 Favorable scores for all questions for men with tenure between 1-2 years
n = 459
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "1-2 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "1-2 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.9102845
## 2 ali_2 0.8231441
## 3 ali_3 0.6710240
## 4 col_1 0.8518519
## 5 col_2 0.6228070
## 6 col_3 0.8358862
## 7 eng_1 0.9387309
## 8 eng_2 0.8711790
## 9 eng_3 0.8275109
## 10 eng_4 0.6914661
## 11 eng_5 0.7227074
## 12 inc_1 0.9080963
## 13 inc_2 0.7550562
## 14 inc_3 0.8505495
## 15 inc_4 0.7676991
## 16 inc_5 0.7802198
## 17 lea_1 0.8769231
## 18 lea_2 0.8796499
## 19 lea_3 0.8903509
## 20 lea_4 0.9082969favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 91 Alignment
## 2 ali_2 82 Alignment
## 3 ali_3 67 Alignment
## 4 col_1 85 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 84 Collaboration
## 7 eng_1 94 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 83 Engagement
## 10 eng_4 69 Engagement
## 11 eng_5 72 Engagement
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 85 Inclusion
## 15 inc_4 77 Inclusion
## 16 inc_5 78 Inclusion
## 17 lea_1 88 Leadership
## 18 lea_2 88 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 91 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8014842
## 2 col 0.7701817
## 3 eng 0.8103189
## 4 inc 0.8123242
## 5 lea 0.8888052#change the factor favorable scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 80 Alignment
## 1 ali_1 91 Alignment
## 2 ali_2 82 Alignment
## 3 ali_3 67 Alignment
## 22 col 77 Collaboration
## 4 col_1 85 Collaboration
## 5 col_2 62 Collaboration
## 6 col_3 84 Collaboration
## 23 eng 81 Engagement
## 7 eng_1 94 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 83 Engagement
## 10 eng_4 69 Engagement
## 11 eng_5 72 Engagement
## 24 inc 81 Inclusion
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 85 Inclusion
## 15 inc_4 77 Inclusion
## 16 inc_5 78 Inclusion
## 25 lea 89 Leadership
## 17 lea_1 88 Leadership
## 18 lea_2 88 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 91 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 1-2 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 1-2 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "1-2 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 1-2 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.15 n. favorable scores for all questions for men with tenure between 2-4 years
n = 453
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "2-4 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "2-4 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8953229
## 2 ali_2 0.8459821
## 3 ali_3 0.6674107
## 4 col_1 0.8177778
## 5 col_2 0.5915179
## 6 col_3 0.7438753
## 7 eng_1 0.9247788
## 8 eng_2 0.8716814
## 9 eng_3 0.7977778
## 10 eng_4 0.6615044
## 11 eng_5 0.7323009
## 12 inc_1 0.9159292
## 13 inc_2 0.7652370
## 14 inc_3 0.8224719
## 15 inc_4 0.7042889
## 16 inc_5 0.7645740
## 17 lea_1 0.8466667
## 18 lea_2 0.8530067
## 19 lea_3 0.8824834
## 20 lea_4 0.8933333favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 90 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 67 Alignment
## 4 col_1 82 Collaboration
## 5 col_2 59 Collaboration
## 6 col_3 74 Collaboration
## 7 eng_1 92 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 80 Engagement
## 10 eng_4 66 Engagement
## 11 eng_5 73 Engagement
## 12 inc_1 92 Inclusion
## 13 inc_2 77 Inclusion
## 14 inc_3 82 Inclusion
## 15 inc_4 70 Inclusion
## 16 inc_5 76 Inclusion
## 17 lea_1 85 Leadership
## 18 lea_2 85 Leadership
## 19 lea_3 88 Leadership
## 20 lea_4 89 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8029053
## 2 col 0.7177236
## 3 eng 0.7976087
## 4 inc 0.7945002
## 5 lea 0.8688725#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 80 Alignment
## 1 ali_1 90 Alignment
## 2 ali_2 85 Alignment
## 3 ali_3 67 Alignment
## 22 col 72 Collaboration
## 4 col_1 82 Collaboration
## 5 col_2 59 Collaboration
## 6 col_3 74 Collaboration
## 23 eng 80 Engagement
## 7 eng_1 92 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 80 Engagement
## 10 eng_4 66 Engagement
## 11 eng_5 73 Engagement
## 24 inc 79 Inclusion
## 12 inc_1 92 Inclusion
## 13 inc_2 77 Inclusion
## 14 inc_3 82 Inclusion
## 15 inc_4 70 Inclusion
## 16 inc_5 76 Inclusion
## 25 lea 87 Leadership
## 17 lea_1 85 Leadership
## 18 lea_2 85 Leadership
## 19 lea_3 88 Leadership
## 20 lea_4 89 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 2-4 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 2-4 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "2-4 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 2-4 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.16 Favorable scores for all questions for men with tenure between 4-6 years
n = 216
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "4-6 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "4-6 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.8930233
## 2 ali_2 0.8262911
## 3 ali_3 0.7289720
## 4 col_1 0.8465116
## 5 col_2 0.6525822
## 6 col_3 0.7428571
## 7 eng_1 0.9120370
## 8 eng_2 0.8697674
## 9 eng_3 0.8000000
## 10 eng_4 0.6744186
## 11 eng_5 0.6481481
## 12 inc_1 0.9052133
## 13 inc_2 0.7572816
## 14 inc_3 0.8564593
## 15 inc_4 0.7439614
## 16 inc_5 0.7403846
## 17 lea_1 0.7943925
## 18 lea_2 0.8651163
## 19 lea_3 0.8925234
## 20 lea_4 0.8738318favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 89 Alignment
## 2 ali_2 83 Alignment
## 3 ali_3 73 Alignment
## 4 col_1 85 Collaboration
## 5 col_2 65 Collaboration
## 6 col_3 74 Collaboration
## 7 eng_1 91 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 80 Engagement
## 10 eng_4 67 Engagement
## 11 eng_5 65 Engagement
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 86 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 74 Inclusion
## 17 lea_1 79 Leadership
## 18 lea_2 87 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 87 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8160954
## 2 col 0.7473170
## 3 eng 0.7808742
## 4 inc 0.8006600
## 5 lea 0.8564660#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 82 Alignment
## 1 ali_1 89 Alignment
## 2 ali_2 83 Alignment
## 3 ali_3 73 Alignment
## 22 col 75 Collaboration
## 4 col_1 85 Collaboration
## 5 col_2 65 Collaboration
## 6 col_3 74 Collaboration
## 23 eng 78 Engagement
## 7 eng_1 91 Engagement
## 8 eng_2 87 Engagement
## 9 eng_3 80 Engagement
## 10 eng_4 67 Engagement
## 11 eng_5 65 Engagement
## 24 inc 80 Inclusion
## 12 inc_1 91 Inclusion
## 13 inc_2 76 Inclusion
## 14 inc_3 86 Inclusion
## 15 inc_4 74 Inclusion
## 16 inc_5 74 Inclusion
## 25 lea 86 Leadership
## 17 lea_1 79 Leadership
## 18 lea_2 87 Leadership
## 19 lea_3 89 Leadership
## 20 lea_4 87 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 4-6 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 4-6 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "4-6 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 4-6 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.3.17 Favorable scores for all questions for men with tenure between 6-10 years
n = 126
table(df_2$gender, df_2$tenure_group)##
## 1-2 years 10+ years 2-4 years 3-6 months 4-6 years 6-10 years
## Female 211 1 191 116 58 37
## Male 459 3 453 209 216 126
##
## 6-12 months Under 3 months
## Female 181 25
## Male 325 39df_2[
df_2$gender == "Male" &
df_2$tenure_group == "6-10 years" &
!is.na(df_2$gender), ]# Calculate favorability_score for columns 2 to 21 by using function_favorability I created.
favorability_score <- function_favorability(df_2[
df_2$gender == "Male" &
df_2$tenure_group == "6-10 years" , 2:21])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 ali_1 0.9354839
## 2 ali_2 0.8943089
## 3 ali_3 0.7723577
## 4 col_1 0.8492063
## 5 col_2 0.7200000
## 6 col_3 0.7680000
## 7 eng_1 0.9365079
## 8 eng_2 0.8888889
## 9 eng_3 0.7698413
## 10 eng_4 0.6904762
## 11 eng_5 0.7200000
## 12 inc_1 0.8861789
## 13 inc_2 0.7166667
## 14 inc_3 0.7786885
## 15 inc_4 0.6290323
## 16 inc_5 0.7704918
## 17 lea_1 0.8080000
## 18 lea_2 0.8548387
## 19 lea_3 0.8480000
## 20 lea_4 0.8560000favorability_score_percent <- favorability_score
favorability_score_percent$favorability_score <- round(favorability_score_percent$favorability_score,2)*100
library(dplyr)
favorability_score_percent$Factor <- dplyr::case_when(
favorability_score_percent$question_number %in% c("ali_1", "ali_2", "ali_3") ~ "Alignment",
favorability_score_percent$question_number %in% c("col_1", "col_2", "col_3") ~ "Collaboration",
favorability_score_percent$question_number %in% c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5") ~ "Engagement",
favorability_score_percent$question_number %in% c("inc_1", "inc_2", "inc_3", "inc_4", "inc_5") ~ "Inclusion",
favorability_score_percent$question_number %in% c("lea_1", "lea_2", "lea_3", "lea_4") ~ "Leadership",
TRUE ~ "Other")
print(favorability_score_percent)## question_number favorability_score Factor
## 1 ali_1 94 Alignment
## 2 ali_2 89 Alignment
## 3 ali_3 77 Alignment
## 4 col_1 85 Collaboration
## 5 col_2 72 Collaboration
## 6 col_3 77 Collaboration
## 7 eng_1 94 Engagement
## 8 eng_2 89 Engagement
## 9 eng_3 77 Engagement
## 10 eng_4 69 Engagement
## 11 eng_5 72 Engagement
## 12 inc_1 89 Inclusion
## 13 inc_2 72 Inclusion
## 14 inc_3 78 Inclusion
## 15 inc_4 63 Inclusion
## 16 inc_5 77 Inclusion
## 17 lea_1 81 Leadership
## 18 lea_2 85 Leadership
## 19 lea_3 85 Leadership
## 20 lea_4 86 Leadership## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(factor_favorable_score,
data.frame(factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score))
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score
## 1 ali 0.8673835
## 2 col 0.7790688
## 3 eng 0.8011429
## 4 inc 0.7562116
## 5 lea 0.8417097#change the favorability scores into percentage
factor_favorability_score_percent <- factor_favorable_score
factor_favorability_score_percent$factor_favorable_score <- round(factor_favorability_score_percent$factor_favorable_score,2)*100
library(dplyr)
factor_favorability_score_percent$Factor <- dplyr::case_when(
factor_favorability_score_percent$factor_abbreviation %in% c("ali") ~ "Alignment",
factor_favorability_score_percent$factor_abbreviation %in% c("col") ~ "Collaboration",
factor_favorability_score_percent$factor_abbreviation %in% c("eng") ~ "Engagement",
factor_favorability_score_percent$factor_abbreviation %in% c("inc") ~ "Inclusion",
factor_favorability_score_percent$factor_abbreviation %in% c("lea") ~ "Leadership",
TRUE ~ "Other")
#combine the table with favorable score for each item with the table with favorable score for each factor
colnames(factor_favorability_score_percent) <- c("question_number", "favorability_score", "Factor")
combined_favorability_df <- rbind(favorability_score_percent, factor_favorability_score_percent)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Factor
## 21 ali 87 Alignment
## 1 ali_1 94 Alignment
## 2 ali_2 89 Alignment
## 3 ali_3 77 Alignment
## 22 col 78 Collaboration
## 4 col_1 85 Collaboration
## 5 col_2 72 Collaboration
## 6 col_3 77 Collaboration
## 23 eng 80 Engagement
## 7 eng_1 94 Engagement
## 8 eng_2 89 Engagement
## 9 eng_3 77 Engagement
## 10 eng_4 69 Engagement
## 11 eng_5 72 Engagement
## 24 inc 76 Inclusion
## 12 inc_1 89 Inclusion
## 13 inc_2 72 Inclusion
## 14 inc_3 78 Inclusion
## 15 inc_4 63 Inclusion
## 16 inc_5 77 Inclusion
## 25 lea 84 Leadership
## 17 lea_1 81 Leadership
## 18 lea_2 85 Leadership
## 19 lea_3 85 Leadership
## 20 lea_4 86 Leadership## Create a ggplot bar plot with the preselected colors
library(ggplot2)
ggplot(combined_favorability_df, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 6-10 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
## Create a ggplot bar plot for factor favorable scores
library(ggplot2)
ggplot(factor_favorability_score_percent, aes(x = question_number, y = favorability_score, fill = Factor)) +
geom_bar(stat = "identity", show.legend = TRUE) +
labs(x = "Question Item", y = "Favorability Score", title = "Favorable Score for Each Factor and Question Item for Men with Tenure between 6-10 years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5) + # Add text labels to the bars
scale_fill_manual(values = group_colors) # Apply custom colors to the bars
#create a df storing each group's engagement factor favorability score gradually.
engagement_by_tenure <- data.frame(
question_number = "6-10 years",
favorability_score = factor_favorable_score$factor_favorable_score[factor_favorable_score$factor_abbreviation == "eng"])
engagement_by_tenureengagement_favorable_score_for_all <- combined_favorability_df[combined_favorability_df$question_number %in% c("eng", "eng_1", "eng_2", "eng_3", "eng_4", "eng_5"), ]
engagement_favorable_score_for_all# Distinguish the average row from other item rows
engagement_favorable_score_for_all$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_score_for_all$question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
ggplot(engagement_favorable_score_for_all, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Factor Items", y = "Favorability Score", title = "Engagement Factor Favorable Scores for Men with Tenure between 6-10 Years") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(favorability_score, "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) 
3.4 Correlate All Non-Engagement Questions to the Engagement Factor for Hooli Overall.
3.4.1 For all employees & all non-engagement items (Kendall’s tau-b & pearson’s r)
The results from the correlations calculated by conducting Kendall’s tau-b and pearson’s r are largley convergent while kendall’s tau-b’s results are much more conservative in terms of the size of the correlations.
#Create a new variable named 'engagement_factor', representing the average engagement scores.
df_2$engagement_factor <- rowMeans(df_2[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
#Select only the numeric columns from 'df_2' excluding 'eng_1' to 'eng_5'
numeric_df_2 <- Filter(is.numeric, df_2)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
library(corrplot)## corrplot 0.92 loaded# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement", caption = "Correlations calculated using Pearson's r"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2 %>%
mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 2,651 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 5 4.6 4.75
## 2 4 4 3.6 3.8 4.75
## 3 4.67 4.67 4.4 5 5
## 4 5 5 5 4.4 5
## 5 4.67 5 4.4 4.8 5
## 6 3.67 4 3.8 3.4 3.25
## 7 3.67 3 3.4 4 5
## 8 4.67 4.67 4.6 4.6 5
## 9 4.67 4.33 5 4.8 5
## 10 5 4.67 4.4 4.6 4.5
## # ℹ 2,641 more rowscomposite_factor_scores <- subset(composite_scores, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 2,651 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 4.6 4.75
## 2 4 4 3.8 4.75
## 3 4.67 4.67 5 5
## 4 5 5 4.4 5
## 5 4.67 5 4.8 5
## 6 3.67 4 3.4 3.25
## 7 3.67 3 4 5
## 8 4.67 4.67 4.6 5
## 9 4.67 4.33 4.8 5
## 10 5 4.67 4.6 4.5
## # ℹ 2,641 more rowsengagement_factor_scores <- subset(composite_scores, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 2,651 × 1
## Engagement
## <dbl>
## 1 5
## 2 3.6
## 3 4.4
## 4 5
## 5 4.4
## 6 3.8
## 7 3.4
## 8 4.6
## 9 5
## 10 4.4
## # ℹ 2,641 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.2 For women only (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
library(dplyr)
# Subset df_2 to include only female employees
df_2_female <- df_2 %>%
filter(gender == "Female")
# Calculate the engagement factor for female employees
df_2_female$engagement_factor <- rowMeans(df_2_female[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
numeric_df_2 <- Filter(is.numeric, df_2_female)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Women", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Women", caption = "Correlations calculated using Pearson's r"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2[df_2$gender == "Female", ] %>%
mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 821 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3.67 4 3.8 3.4 3.25
## 2 5 4.67 4.4 4.6 4.5
## 3 4.33 4.33 4.8 4.6 5
## 4 4.33 4.33 5 4.4 4.25
## 5 4.33 4 3.8 4.6 4.75
## 6 4.33 4 4.2 3.4 4
## 7 5 5 5 4.6 5
## 8 2 3.33 2.6 2.8 4
## 9 4.67 4.33 3.6 5 4.25
## 10 4 4 4.4 4.4 4
## # ℹ 811 more rowscomposite_factor_scores <- subset(composite_scores, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 821 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 3.67 4 3.4 3.25
## 2 5 4.67 4.6 4.5
## 3 4.33 4.33 4.6 5
## 4 4.33 4.33 4.4 4.25
## 5 4.33 4 4.6 4.75
## 6 4.33 4 3.4 4
## 7 5 5 4.6 5
## 8 2 3.33 2.8 4
## 9 4.67 4.33 5 4.25
## 10 4 4 4.4 4
## # ℹ 811 more rowsengagement_factor_scores <- subset(composite_scores, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 821 × 1
## Engagement
## <dbl>
## 1 3.8
## 2 4.4
## 3 4.8
## 4 5
## 5 3.8
## 6 4.2
## 7 5
## 8 2.6
## 9 3.6
## 10 4.4
## # ℹ 811 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for Women", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.3 For men only (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
library(dplyr)
# Subset df_2 to include only male employees
df_2_male <- df_2 %>%
filter(gender == "Male")
# Calculate the engagement factor for female employees
df_2_male$engagement_factor <- rowMeans(df_2_male[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
# Display a few rows to check the new variable
head(df_2_male)numeric_df_2 <- Filter(is.numeric, df_2_male)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Men", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Men", caption = "Correlations calculated using Pearson's r"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2[df_2$gender == "Male", ] %>%
mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 1,831 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 5 4.6 4.75
## 2 4 4 3.6 3.8 4.75
## 3 4.67 4.67 4.4 5 5
## 4 5 5 5 4.4 5
## 5 4.67 5 4.4 4.8 5
## 6 3.67 3 3.4 4 5
## 7 4.67 4.67 4.6 4.6 5
## 8 4.67 4.33 5 4.8 5
## 9 4.67 5 4 5 5
## 10 4 3.67 4 3.6 4
## # ℹ 1,821 more rowscomposite_factor_scores <- subset(composite_scores, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 1,831 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 4.6 4.75
## 2 4 4 3.8 4.75
## 3 4.67 4.67 5 5
## 4 5 5 4.4 5
## 5 4.67 5 4.8 5
## 6 3.67 3 4 5
## 7 4.67 4.67 4.6 5
## 8 4.67 4.33 4.8 5
## 9 4.67 5 5 5
## 10 4 3.67 3.6 4
## # ℹ 1,821 more rowsengagement_factor_scores <- subset(composite_scores, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 1,831 × 1
## Engagement
## <dbl>
## 1 5
## 2 3.6
## 3 4.4
## 4 5
## 5 4.4
## 6 3.4
## 7 4.6
## 8 5
## 9 4
## 10 4
## # ℹ 1,821 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for Men", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.4 For Indian employees (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
library(dplyr)
# Subset df_2 to include only Indian employees
df_2_india <- df_2 %>%
filter(country == "India")
# Calculate the engagement factor for female employees
df_2_india$engagement_factor <- rowMeans(df_2_india[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
# Display a few rows to check the new variable
head(df_2_india)numeric_df_2 <- Filter(is.numeric, df_2_india)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Indian Employees", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Indian Employees", caption = "Correlations calculated using Pearson's r"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2[df_2$country == "India", ] %>%
mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 288 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 5 4 5 5
## 2 4 4 4.2 5 5
## 3 5 5 4.6 5 5
## 4 4.67 4.33 3.6 5 4.25
## 5 5 5 5 5 5
## 6 3.33 4 4.8 3.6 4
## 7 4.67 5 4.2 5 5
## 8 3 3.67 4.6 4.2 3.5
## 9 4 4 4.8 4 4
## 10 4.67 5 5 5 5
## # ℹ 278 more rowscomposite_factor_scores <- subset(composite_scores, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 288 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 4.67 5 5 5
## 2 4 4 5 5
## 3 5 5 5 5
## 4 4.67 4.33 5 4.25
## 5 5 5 5 5
## 6 3.33 4 3.6 4
## 7 4.67 5 5 5
## 8 3 3.67 4.2 3.5
## 9 4 4 4 4
## 10 4.67 5 5 5
## # ℹ 278 more rowsengagement_factor_scores <- subset(composite_scores, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 288 × 1
## Engagement
## <dbl>
## 1 4
## 2 4.2
## 3 4.6
## 4 3.6
## 5 5
## 6 4.8
## 7 4.2
## 8 4.6
## 9 4.8
## 10 5
## # ℹ 278 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for Indian Employees", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.5 For German employees (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
library(dplyr)
# Subset df_2 to include only Indian employees
df_2_germany <- df_2 %>%
filter(country == "Germany")
# Calculate the engagement factor for german employees
df_2_germany$engagement_factor <- rowMeans(df_2_germany[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
# Display a few rows to check the new variable
head(df_2_germany)numeric_df_2 <- Filter(is.numeric, df_2_germany)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for German Employees", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for German Employees", caption = "Correlations calculated using Pearson's r"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2[df_2$country == "Germany", ] %>%
mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 48 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3.33 3.8 4 4
## 2 4 5 4.4 3.8 5
## 3 3.67 3 3.6 2.8 3.5
## 4 3.67 3.33 3.4 3.6 4
## 5 4.33 4 4.4 5 4.75
## 6 4.33 2.67 3.2 3.4 2.75
## 7 4 3.67 4.4 4.8 4.25
## 8 4 3.33 4 5 5
## 9 5 4.33 4.6 5 5
## 10 3.67 3 3 5 4
## # ℹ 38 more rowscomposite_factor_scores <- subset(composite_scores, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 48 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3.33 4 4
## 2 4 5 3.8 5
## 3 3.67 3 2.8 3.5
## 4 3.67 3.33 3.6 4
## 5 4.33 4 5 4.75
## 6 4.33 2.67 3.4 2.75
## 7 4 3.67 4.8 4.25
## 8 4 3.33 5 5
## 9 5 4.33 5 5
## 10 3.67 3 5 4
## # ℹ 38 more rowsengagement_factor_scores <- subset(composite_scores, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 48 × 1
## Engagement
## <dbl>
## 1 3.8
## 2 4.4
## 3 3.6
## 4 3.4
## 5 4.4
## 6 3.2
## 7 4.4
## 8 4
## 9 4.6
## 10 3
## # ℹ 38 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for German Employees", caption = "Correlations calculated using Kendall's tau-b"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.6 For employees who are high on employee_engagement (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
This analysis included survey responses onlyfrom employees whose engagement factor composite score is equal to or greater than 4 only.
library(dplyr)
# Calculate the engagement factor for employees
df_2$engagement_factor <- rowMeans(df_2[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
hist(df_2$engagement_factor)
high_engagement <- df_2 %>%
filter(engagement_factor >= 4)
numeric_df_2 <- Filter(is.numeric, high_engagement)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for highly engaging employees", caption = "Correlations calculated using Kendall's tau-b | employees with engagement composite factor score greater than or equal to 4 are only included for analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Highly Engaged Employees", caption = "Correlations calculated using Pearson's r | employees with engagement composite factor score greater than or equal to 4 are only included for analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2 %>% mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 2,651 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 5 4.6 4.75
## 2 4 4 3.6 3.8 4.75
## 3 4.67 4.67 4.4 5 5
## 4 5 5 5 4.4 5
## 5 4.67 5 4.4 4.8 5
## 6 3.67 4 3.8 3.4 3.25
## 7 3.67 3 3.4 4 5
## 8 4.67 4.67 4.6 4.6 5
## 9 4.67 4.33 5 4.8 5
## 10 5 4.67 4.4 4.6 4.5
## # ℹ 2,641 more rowscomposite_scores_high <- composite_scores %>%
filter(Engagement >= 4)
composite_factor_scores <- subset(composite_scores_high, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 1,819 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 4.6 4.75
## 2 4.67 4.67 5 5
## 3 5 5 4.4 5
## 4 4.67 5 4.8 5
## 5 4.67 4.67 4.6 5
## 6 4.67 4.33 4.8 5
## 7 5 4.67 4.6 4.5
## 8 4.67 5 5 5
## 9 4 3.67 3.6 4
## 10 NaN NaN NaN NaN
## # ℹ 1,809 more rowsengagement_factor_scores <- subset(composite_scores_high, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 1,819 × 1
## Engagement
## <dbl>
## 1 5
## 2 4.4
## 3 5
## 4 4.4
## 5 4.6
## 6 5
## 7 4.4
## 8 4
## 9 4
## 10 4
## # ℹ 1,809 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for Highly Engaged Employees", caption = "Correlations calculated using Kendall's tau-b | employees with engagement composite factor score greater than or equal to 4 are only included for analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.4.7 For employees who are low on employee_engagement (correlation calculated by conducting kendall’s tau-b & Pearson’s r)
This analysis included survey responses only from those
employees whose engagement factor composite score is smaller than or
equal to 2. n = 47
library(dplyr)
# Calculate the engagement factor for employees
df_2$engagement_factor <- rowMeans(df_2[, c("eng_1", "eng_2", "eng_3", "eng_4", "eng_5")], na.rm = TRUE)
hist(df_2$engagement_factor)
low_engagement <- df_2 %>%
filter(engagement_factor <= 2)
numeric_df_2 <- Filter(is.numeric, low_engagement)
library(dplyr)
numeric_df_2 <- subset(numeric_df_2, select= -c(eng_1, eng_2, eng_3, eng_4, eng_5))
#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(numeric_df_2, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "engagement_factor"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "engagement_factor",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[13:15, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Less Engaged employees", caption = "Correlations calculated using Kendall's tau-b | Only employees with engagement composite factor score lower than or equal to 2 are included for this analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
# Recreate the Pearson's r correlation matrix to see if they are too divergent
correlation_matrix <- cor(numeric_df_2, use = "complete.obs")
corrplot(correlation_matrix, type = "upper", order = "hclust")
# Extract correlations for "engagement_factor"
engagement_correlations <- correlation_matrix[, "engagement_factor"]
engagement_factor_correlation_table <- data.frame(
variable = names(engagement_correlations),
correlation = engagement_correlations
)
engagement_factor_correlation_table <- engagement_factor_correlation_table[
engagement_factor_correlation_table$variable != "engagement_factor",
]
# Sort in descending order to get the strongest correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table %>%
arrange(correlation)
# Keep only the top 3 correlations
engagement_factor_correlation_table <- engagement_factor_correlation_table[13:15,]
# Reorder the factor levels
engagement_factor_correlation_table$variable <- factor(
engagement_factor_correlation_table$variable,
levels = engagement_factor_correlation_table$variable
)
# Create a ggplot with horizontal bars for the top 3 Pearson's r correlations
ggplot(engagement_factor_correlation_table, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Top 3 Factors with Strongest Associations to Employee Engagement for Less Engaged Employees", caption = "Correlations calculated using Pearson's r | Only employees with engagement composite factor score lower than or equal to 2 are included for this analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
#engagement factor's correlations with other factors
library(dplyr)
#Create a df with composite factor scores for each group of items
composite_scores <- df_2 %>% mutate(
Alignment = rowMeans(select(., ali_1:ali_3), na.rm = TRUE), # Alignment score
Collaboration = rowMeans(select(., col_1:col_3), na.rm = TRUE), # Collaboration score
Engagement = rowMeans(select(., eng_1:eng_5), na.rm = TRUE), # Engagement score
Inclusion = rowMeans(select(., inc_1:inc_5), na.rm = TRUE), # Inclusion score
Leadership = rowMeans(select(., lea_1:lea_4), na.rm = TRUE) # Leadership score
) %>%
# Select only the composite factor scores and an identifier (if needed)
select(Alignment, Collaboration, Engagement, Inclusion, Leadership)
# Print the new data frame to ensure it contains the expected composite scores
print(composite_scores)## # A tibble: 2,651 × 5
## Alignment Collaboration Engagement Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4.67 3 5 4.6 4.75
## 2 4 4 3.6 3.8 4.75
## 3 4.67 4.67 4.4 5 5
## 4 5 5 5 4.4 5
## 5 4.67 5 4.4 4.8 5
## 6 3.67 4 3.8 3.4 3.25
## 7 3.67 3 3.4 4 5
## 8 4.67 4.67 4.6 4.6 5
## 9 4.67 4.33 5 4.8 5
## 10 5 4.67 4.4 4.6 4.5
## # ℹ 2,641 more rowscomposite_scores_low <- composite_scores %>%
filter(Engagement <= 2)
composite_factor_scores <- subset(composite_scores_low, select= c(Alignment, Collaboration, Inclusion, Leadership))
print(composite_factor_scores)## # A tibble: 47 × 4
## Alignment Collaboration Inclusion Leadership
## <dbl> <dbl> <dbl> <dbl>
## 1 3.33 4 4.2 1.75
## 2 2 1 2.2 3.25
## 3 2.33 3.33 2 2
## 4 3 4 3.6 3
## 5 1.67 4 2.8 2.5
## 6 2.33 2 3 1
## 7 2.33 3 3.8 3
## 8 1.67 3.67 2.6 4.5
## 9 3 2.67 3 2.5
## 10 1.67 2.67 2.8 2.75
## # ℹ 37 more rowsengagement_factor_scores <- subset(composite_scores_low, select= -c(Alignment, Collaboration, Inclusion, Leadership))
print(engagement_factor_scores)## # A tibble: 47 × 1
## Engagement
## <dbl>
## 1 1.8
## 2 1.2
## 3 1.8
## 4 2
## 5 2
## 6 1.4
## 7 2
## 8 1.8
## 9 2
## 10 1.8
## # ℹ 37 more rows#Conduct Kendall's tau-b correlation to get correlations
correlation_matrix_kendall <- cor(composite_scores, method = "kendall", use = "complete.obs")
#Extract the correlations for "engagement_factor"
engagement_correlations_kendall <- correlation_matrix_kendall[, "Engagement"]
engagement_factor_correlation_table_kendall <- data.frame(
variable = names(engagement_correlations_kendall),
correlation = engagement_correlations_kendall
)
#Remove 'engagement_factor' and sort by correlation in descending order
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[
engagement_factor_correlation_table_kendall$variable != "Engagement",
]
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall %>%
arrange(correlation)
#Keep only the top 3 strongest correlations
engagement_factor_correlation_table_kendall <- engagement_factor_correlation_table_kendall[1:4, ]
# Reorder the factor levels based on the new order
engagement_factor_correlation_table_kendall$variable <- factor(
engagement_factor_correlation_table_kendall$variable,
levels = engagement_factor_correlation_table_kendall$variable
)
# Use scales package to format correlations as percentages
library(scales)
# Create a ggplot with horizontal bars for the top 3 Kendall's tau-b correlations
ggplot(engagement_factor_correlation_table_kendall, aes(x = correlation, y = variable)) +
geom_bar(stat = "identity", fill = "purple", alpha = 0.7) +
geom_text(aes(label = percent(correlation, accuracy = 1)), hjust = -0.1, size = 3.5, color = "black") +
labs(x = "Correlation", y = "Variable", title = "Factors' relationships with Employee Engagement for Less Engaged Employees", caption = "Correlations calculated using Kendall's tau-b | Only employees with engagement composite factor score lower than or equal to 2 are included for analysis"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 10, hjust = 0)
)
3.5 Calculate Engagement & Leadership Factor Scores for All Countries.
3.5.1 Creating a graph comparing factor favorable scores for engagement & leadership across countries
#Create an empty data frame to save the consolidated favorable_scores
consolidated_favorable_scores <- data.frame(
factor_abbreviation = character(),
factor_favorable_score = numeric(),
country = character(),
stringsAsFactors = FALSE
)
#list of countries
countries <- c("Australia", "Canada", "China", "Denmark", "France", "Germany", "India", "United Kingdom", "United States")
#Loop through each country and calculate factor favorable score
for (country in countries) {
#Filter data for the specific country
country_df <- df_2[df_2$country == country & !is.na(df_2$country), ]
#Calculate favorability_score using a custom function
favorability_score <- function_favorability(country_df[, c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number))
#Create a data frame for this country's factor_favorable_score (so a dataframe within a loop)
factor_favorable_score <- data.frame(
factor_abbreviation = character(),
factor_favorable_score = numeric(),
country = character(),
stringsAsFactors = FALSE
)
#Calculate the average favorable score for each factor
for (factor_abbr in factor_abbreviations) {
#Subset favorable scores for the factor
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#Calculate the average favorable score
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE)
#Append the calculated score to the country's data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = country # Add the country label
)
)
}
#Consolidate this country's data frame into the main consolidated data frame
consolidated_favorable_scores <- rbind(
consolidated_favorable_scores,
factor_favorable_score
)
}
print(consolidated_favorable_scores)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8277228 Australia
## 2 lea 0.9257426 Australia
## 3 eng 0.8380952 Canada
## 4 lea 0.8625932 Canada
## 5 eng 0.8066667 China
## 6 lea 0.8500000 China
## 7 eng 0.8083333 Denmark
## 8 lea 0.8618659 Denmark
## 9 eng 0.8338235 France
## 10 lea 0.9301471 France
## 11 eng 0.6962766 Germany
## 12 lea 0.8645833 Germany
## 13 eng 0.8440634 India
## 14 lea 0.9308116 India
## 15 eng 0.8099251 United Kingdom
## 16 lea 0.8995405 United Kingdom
## 17 eng 0.7786955 United States
## 18 lea 0.8336043 United States#Sort the data frame by factor_favorable_score
consolidated_favorable_scores <- consolidated_favorable_scores %>%
arrange(desc(factor_favorable_score))
#Reorder the factor levels based on the sorted data frame
consolidated_favorable_scores$factor_abbreviation <- factor(
consolidated_favorable_scores$factor_abbreviation,
levels = unique(consolidated_favorable_scores$factor_abbreviation)
)
# Load necessary libraries
library(ggplot2)
library(dplyr)
library(scales)
# Filter the data into two subsets: one for 'lea' (leadership factor) and one for 'eng' (engagement factor)
leadership_scores <- consolidated_favorable_scores %>%
filter(factor_abbreviation == "lea")
engagement_scores <- consolidated_favorable_scores %>%
filter(factor_abbreviation == "eng")
# Reorder the factor levels based on the sorted data frame
leadership_scores$country <- factor(
leadership_scores$country,
levels = unique(leadership_scores$country)
)
engagement_scores$country <- factor(
engagement_scores$country,
levels = unique(engagement_scores$country)
)
# Create ggplot for leadership scores
ggplot(leadership_scores, aes(x = country, y = factor_favorable_score * 100, fill = country)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
geom_text(
aes(label = paste0(round(factor_favorable_score * 100, 1), "%")),
position = position_dodge(width = 0.9),
vjust = -0.5 # Position the text above the bars
) +
labs(
x = "Country",
y = "Leadership Favorability Score (%)",
title = "Leadership Factor Favorable Scores by Country (%)"
) +
scale_y_continuous(labels = percent_format(scale = 1)) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5)
)
# Create ggplot for engagement scores
ggplot(engagement_scores, aes(x = country, y = factor_favorable_score * 100, fill = country)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
geom_text(
aes(label = paste0(round(factor_favorable_score * 100, 1), "%")),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3
) +
labs(
x = "Country",
y = "Engagement Favorability Score (%)",
title = "Engagement Factor Favorable Scores by Country (%)"
) +
scale_y_continuous(labels = percent_format(scale = 1)) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5)
)
# Create a dataframe to save the consolidated factor scores
consolidated_favorable_scores <- data.frame(
factor_abbreviation = character(),
factor_favorable_score = numeric(),
country = character(),
stringsAsFactors = FALSE
)
# List of countries
countries <- c("Australia", "Canada", "China", "Denmark", "France", "Germany", "India", "United Kingdom", "United States")
# Loop through each country and calculate factor favorable score
for (country in countries) {
# Filter data for the specific country
country_df <- df_2[df_2$country == country & !is.na(df_2$country), ]
# Calculate favorability_score using a custom function for leadership factor
favorability_score <- function_favorability(country_df[, c(18:21)]) # 'lea' questions
colnames(favorability_score)[1] <- "question_number"
# Create a data frame for this country's factor_favorable_score
factor_favorable_score <- data.frame(
factor_abbreviation = character(),
factor_favorable_score = numeric(),
country = character(),
stringsAsFactors = FALSE
)
# Calculate the average favorable score for each factor
for (factor_abbr in unique(sub("_.*", "", favorability_score$question_number))) {
# Subset favorable scores for the factor
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
# Calculate the average favorable score
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE)
# Append the calculated score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = country # Add the country label
)
)
}
# Consolidate this country's data frame into the main consolidated data frame
consolidated_favorable_scores <- rbind(
consolidated_favorable_scores,
factor_favorable_score
)
}
# Now calculate the company's average leadership score based on all employees
all_employees_df <- df_2 # Full dataset
company_favorability_score <- function_favorability(all_employees_df[, c(18:21)]) # 'lea' questions
company_avg_leadership_score <- mean(company_favorability_score$favorability_score, na.rm = TRUE) # Average leadership factor
# Create a row for the company's average score
company_average_row <- data.frame(
factor_abbreviation = "lea",
factor_favorable_score = company_avg_leadership_score,
country = "Company Average" # Label for the overall company
)
# Add the company's average to the consolidated data frame
consolidated_favorable_scores <- rbind(company_average_row, consolidated_favorable_scores)
# Define the desired order for the countries
desired_order <- c("Company Average", "India", "France", "Australia", "United Kingdom", "Germany", "Canada", "Denmark", "China", "United States")
# Reorder the factor levels for proper display
consolidated_favorable_scores$country <- factor(
consolidated_favorable_scores$country,
levels = desired_order # 'Company Average' first, then other countries in the desired order
)
# Create a ggplot for leadership scores including the company average
ggplot(consolidated_favorable_scores, aes(x = country, y = factor_favorable_score * 100, fill = country)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
geom_text(
aes(label = paste0(round(factor_favorable_score * 100, 1), "%")),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3
) +
labs(
x = "Country",
y = "Leadership Favorability Score (%)",
title = "Leadership Factor Favorable Scores by Country (%)"
) +
scale_y_continuous(labels = scales::percent_format(scale = 1)) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1) # Rotate x-axis labels for readability
)
library(ggplot2)
library(dplyr)
library(scales)
# Create a dataframe to save the consolidated factor scores
consolidated_favorable_scores <- data.frame(
factor_abbreviation = character(),
factor_favorable_score = numeric(),
country = character(),
stringsAsFactors = FALSE
)
# List of countries
countries <- c("India", "Canada", "France", "Australia", "United Kingdom", "Denmark", "China", "United States", "Germany")
# Loop through each country and calculate factor favorable score
for (country in countries) {
# Filter data for the specific country
country_df <- df_2[df_2$country == country & !is.na(df_2$country), ]
# Calculate favorability_score using a custom function for 'eng' questions (Engagement)
favorability_score <- function_favorability(country_df[, c(8:12)])
colnames(favorability_score)[1] <- "question_number"
# Create a data frame for this country's factor_favorable_score
factor_favorable_score <- data.frame(
factor_abbreviation = "eng",
factor_favorable_score = mean(favorability_score$favorability_score, na.rm = TRUE),
country = country
)
# Consolidate this country's data frame into the main consolidated data frame
consolidated_favorable_scores <- rbind(consolidated_favorable_scores, factor_favorable_score)
}
# Now calculate the company's average leadership score based on all employees
all_employees_df <- df_2 # Full dataset
company_favorability_score <- function_favorability(all_employees_df[, c(8:12)]) # 'eng' questions
company_avg_engagement_score <- mean(company_favorability_score$favorability_score, na.rm = TRUE) # Average engagement factor
# Create a row for the company's average score
company_average_row <- data.frame(
factor_abbreviation = "eng",
factor_favorable_score = company_avg_engagement_score,
country = "Company Average" # Label for the overall company
)
# Add the company's average engagement score to the consolidated data frame
company_average_row <- data.frame(
factor_abbreviation = "eng",
factor_favorable_score = company_avg_engagement_score,
country = "Company Average"
)
consolidated_favorable_scores <- rbind(company_average_row, consolidated_favorable_scores)
# Define the desired order for the countries
desired_order <- c("Company Average", "India", "Canada", "France", "Australia", "United Kingdom", "Denmark", "China", "United States", "Germany")
# Reorder the countries for proper display
consolidated_favorable_scores$country <- factor(
consolidated_favorable_scores$country,
levels = desired_order
)
# Create ggplot for engagement scores
ggplot(consolidated_favorable_scores, aes(x = country, y = factor_favorable_score * 100, fill = country)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
geom_text(
aes(label = paste0(round(factor_favorable_score * 100, 1), "%")),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3
) +
labs(
x = "Country",
y = "Engagement Favorability Score (%)",
title = "Engagement Factor Favorable Scores by Country (%)"
) +
scale_y_continuous(labels = percent_format(scale = 1)) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5)
)
3.5.2 Australia’s
n = 101
df_2[df_2$country == "Australia" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "Australia", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9207921
## 2 eng_2 0.9306931
## 3 eng_3 0.8514851
## 4 eng_4 0.6930693
## 5 eng_5 0.7425743
## 6 lea_1 0.8613861
## 7 lea_2 0.9207921
## 8 lea_3 0.9603960
## 9 lea_4 0.9603960# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "Australia" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8277228 Australia
## 2 lea 0.9257426 Australiafavorability_score$Country <- "Australia"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8277228 Australia
## 1 eng_1 0.9207921 Australia
## 2 eng_2 0.9306931 Australia
## 3 eng_3 0.8514851 Australia
## 4 eng_4 0.6930693 Australia
## 5 eng_5 0.7425743 Australia
## 11 lea 0.9257426 Australia
## 6 lea_1 0.8613861 Australia
## 7 lea_2 0.9207921 Australia
## 8 lea_3 0.9603960 Australia
## 9 lea_4 0.9603960 Australia#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8277228 Australia
## 1 eng_1 0.9207921 Australia
## 2 eng_2 0.9306931 Australia
## 3 eng_3 0.8514851 Australia
## 4 eng_4 0.6930693 Australia
## 5 eng_5 0.7425743 Australia# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for Aussie employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for Aussie Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.5.3 Canada’s
n = 84
df_2[df_2$country == "Canada" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "Canada", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9285714
## 2 eng_2 0.8333333
## 3 eng_3 0.8690476
## 4 eng_4 0.7619048
## 5 eng_5 0.7976190
## 6 lea_1 0.8452381
## 7 lea_2 0.8333333
## 8 lea_3 0.9404762
## 9 lea_4 0.8313253# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "Canada" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8380952 Canada
## 2 lea 0.8625932 Canadafavorability_score$Country <- "Canada"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8380952 Canada
## 1 eng_1 0.9285714 Canada
## 2 eng_2 0.8333333 Canada
## 3 eng_3 0.8690476 Canada
## 4 eng_4 0.7619048 Canada
## 5 eng_5 0.7976190 Canada
## 11 lea 0.8625932 Canada
## 6 lea_1 0.8452381 Canada
## 7 lea_2 0.8333333 Canada
## 8 lea_3 0.9404762 Canada
## 9 lea_4 0.8313253 Canada#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8380952 Canada
## 1 eng_1 0.9285714 Canada
## 2 eng_2 0.8333333 Canada
## 3 eng_3 0.8690476 Canada
## 4 eng_4 0.7619048 Canada
## 5 eng_5 0.7976190 Canada# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for Danish employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for Canadian Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1))
3.5.4 China’s
n = 60
df_2[df_2$country == "China" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "China", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.8666667
## 2 eng_2 0.7833333
## 3 eng_3 0.7833333
## 4 eng_4 0.7833333
## 5 eng_5 0.8166667
## 6 lea_1 0.8333333
## 7 lea_2 0.8166667
## 8 lea_3 0.8833333
## 9 lea_4 0.8666667# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "China" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8066667 China
## 2 lea 0.8500000 Chinafavorability_score$Country <- "China"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8066667 China
## 1 eng_1 0.8666667 China
## 2 eng_2 0.7833333 China
## 3 eng_3 0.7833333 China
## 4 eng_4 0.7833333 China
## 5 eng_5 0.8166667 China
## 11 lea 0.8500000 China
## 6 lea_1 0.8333333 China
## 7 lea_2 0.8166667 China
## 8 lea_3 0.8833333 China
## 9 lea_4 0.8666667 China#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8066667 China
## 1 eng_1 0.8666667 China
## 2 eng_2 0.7833333 China
## 3 eng_3 0.7833333 China
## 4 eng_4 0.7833333 China
## 5 eng_5 0.8166667 China# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for Chinese employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for Chinese Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1))
3.5.5 Denmark’s
n = 24
df_2[df_2$country == "Denmark" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "Denmark", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9583333
## 2 eng_2 0.7916667
## 3 eng_3 0.8333333
## 4 eng_4 0.7083333
## 5 eng_5 0.7500000
## 6 lea_1 0.8333333
## 7 lea_2 0.8750000
## 8 lea_3 0.8695652
## 9 lea_4 0.8695652# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "Denmark" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8083333 Denmark
## 2 lea 0.8618659 Denmarkfavorability_score$Country <- "Denmark"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8083333 Denmark
## 1 eng_1 0.9583333 Denmark
## 2 eng_2 0.7916667 Denmark
## 3 eng_3 0.8333333 Denmark
## 4 eng_4 0.7083333 Denmark
## 5 eng_5 0.7500000 Denmark
## 11 lea 0.8618659 Denmark
## 6 lea_1 0.8333333 Denmark
## 7 lea_2 0.8750000 Denmark
## 8 lea_3 0.8695652 Denmark
## 9 lea_4 0.8695652 Denmark#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8083333 Denmark
## 1 eng_1 0.9583333 Denmark
## 2 eng_2 0.7916667 Denmark
## 3 eng_3 0.8333333 Denmark
## 4 eng_4 0.7083333 Denmark
## 5 eng_5 0.7500000 Denmark# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for Danish employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for Danish Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.5.6 France’s
n = 136
df_2[df_2$country == "France" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "France", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9338235
## 2 eng_2 0.8602941
## 3 eng_3 0.8750000
## 4 eng_4 0.7279412
## 5 eng_5 0.7720588
## 6 lea_1 0.9264706
## 7 lea_2 0.9044118
## 8 lea_3 0.9411765
## 9 lea_4 0.9485294# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "France" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8338235 France
## 2 lea 0.9301471 Francefavorability_score$Country <- "France"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8338235 France
## 1 eng_1 0.9338235 France
## 2 eng_2 0.8602941 France
## 3 eng_3 0.8750000 France
## 4 eng_4 0.7279412 France
## 5 eng_5 0.7720588 France
## 11 lea 0.9301471 France
## 6 lea_1 0.9264706 France
## 7 lea_2 0.9044118 France
## 8 lea_3 0.9411765 France
## 9 lea_4 0.9485294 France#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8338235 France
## 1 eng_1 0.9338235 France
## 2 eng_2 0.8602941 France
## 3 eng_3 0.8750000 France
## 4 eng_4 0.7279412 France
## 5 eng_5 0.7720588 France# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for French employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for French Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.5.7 Germany’s
n = 48
df_2[df_2$country == "Germany" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "Germany", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.8333333
## 2 eng_2 0.8333333
## 3 eng_3 0.7083333
## 4 eng_4 0.5106383
## 5 eng_5 0.5957447
## 6 lea_1 0.8750000
## 7 lea_2 0.7916667
## 8 lea_3 0.9375000
## 9 lea_4 0.8541667# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "Germany" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.6962766 Germany
## 2 lea 0.8645833 Germanyfavorability_score$Country <- "Germany"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.6962766 Germany
## 1 eng_1 0.8333333 Germany
## 2 eng_2 0.8333333 Germany
## 3 eng_3 0.7083333 Germany
## 4 eng_4 0.5106383 Germany
## 5 eng_5 0.5957447 Germany
## 11 lea 0.8645833 Germany
## 6 lea_1 0.8750000 Germany
## 7 lea_2 0.7916667 Germany
## 8 lea_3 0.9375000 Germany
## 9 lea_4 0.8541667 Germany#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.6962766 Germany
## 1 eng_1 0.8333333 Germany
## 2 eng_2 0.8333333 Germany
## 3 eng_3 0.7083333 Germany
## 4 eng_4 0.5106383 Germany
## 5 eng_5 0.5957447 Germany# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for German employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for German Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
#Desired order of question_number in reverse as I am making a horizontal graph
desired_order <- c("eng_5", "eng_4", "eng_3", "eng_2", "eng_1", "eng")
#Set the factor levels in reverse order
engagement_favorable_scores$question_number <- factor(
engagement_favorable_scores$question_number,
levels = (desired_order) # Reverse the desired order
)
#Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_score_for_all$question_number == engagement_favorable_scores$question_number[1], "first", "other")
#Color palette for highlighting
highlight_colors <- c("first" = "#9A36B2", # Purple for the 'first' category
"other" = "#C9A5DD") # light purple for the 'other' category
#Create a horizontal bar plot with ggplot
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score*100, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE) +
coord_flip() +
labs(
x = "Factor Items",
y = "Favorability Score (%)",
title = "Engagement Factor Favorable Scores for Employees in Germany",
caption = "Favorability score represents the percentage of respondents who agreed or strongly agreed with the question item over all non-missing responses. Factor favorability score is an average of all the items for the factor."
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
plot.caption = element_text(size = 6, hjust = 0)
) +
geom_text(aes(label = paste0(round(favorability_score*100), "%")),
hjust = -0.3,
size = 4) +
scale_fill_manual(values = highlight_colors) 
3.5.8 India’s
n = 288
df_2[df_2$country == "India" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "India", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9442509
## 2 eng_2 0.9166667
## 3 eng_3 0.8767606
## 4 eng_4 0.7152778
## 5 eng_5 0.7673611
## 6 lea_1 0.8982456
## 7 lea_2 0.9125874
## 8 lea_3 0.9581882
## 9 lea_4 0.9542254# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "India" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8440634 India
## 2 lea 0.9308116 Indiafavorability_score$Country <- "India"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8440634 India
## 1 eng_1 0.9442509 India
## 2 eng_2 0.9166667 India
## 3 eng_3 0.8767606 India
## 4 eng_4 0.7152778 India
## 5 eng_5 0.7673611 India
## 11 lea 0.9308116 India
## 6 lea_1 0.8982456 India
## 7 lea_2 0.9125874 India
## 8 lea_3 0.9581882 India
## 9 lea_4 0.9542254 India#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8440634 India
## 1 eng_1 0.9442509 India
## 2 eng_2 0.9166667 India
## 3 eng_3 0.8767606 India
## 4 eng_4 0.7152778 India
## 5 eng_5 0.7673611 India# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for Indian employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for Indian Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.5.9 United Kingdom’s
n = 359
df_2[df_2$country == "United Kingdom" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "United Kingdom", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9385475
## 2 eng_2 0.8770950
## 3 eng_3 0.8328691
## 4 eng_4 0.6796657
## 5 eng_5 0.7214485
## 6 lea_1 0.8743017
## 7 lea_2 0.8659218
## 8 lea_3 0.9220056
## 9 lea_4 0.9359331# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "United Kingdom" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.8099251 United Kingdom
## 2 lea 0.8995405 United Kingdomfavorability_score$Country <- "United Kingdom"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.8099251 United Kingdom
## 1 eng_1 0.9385475 United Kingdom
## 2 eng_2 0.8770950 United Kingdom
## 3 eng_3 0.8328691 United Kingdom
## 4 eng_4 0.6796657 United Kingdom
## 5 eng_5 0.7214485 United Kingdom
## 11 lea 0.8995405 United Kingdom
## 6 lea_1 0.8743017 United Kingdom
## 7 lea_2 0.8659218 United Kingdom
## 8 lea_3 0.9220056 United Kingdom
## 9 lea_4 0.9359331 United Kingdom#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.8099251 United Kingdom
## 1 eng_1 0.9385475 United Kingdom
## 2 eng_2 0.8770950 United Kingdom
## 3 eng_3 0.8328691 United Kingdom
## 4 eng_4 0.6796657 United Kingdom
## 5 eng_5 0.7214485 United Kingdom# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for U.S. employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for U.K. Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.5.10 United States’
n = 1551
df_2[df_2$country == "United States" & !is.na(df_2$country), ]# Calculate favorability_score for columns 8 to 12 (engagement questions) by using function_favorability I created.
favorability_score <- function_favorability(df_2[df_2$country == "United States", c(8:12, 18:21)])
colnames(favorability_score)[1] <- "question_number"
print(favorability_score)## question_number favorability_score
## 1 eng_1 0.9153200
## 2 eng_2 0.8312864
## 3 eng_3 0.7692806
## 4 eng_4 0.6735751
## 5 eng_5 0.7040155
## 6 lea_1 0.8000000
## 7 lea_2 0.8273616
## 8 lea_3 0.8654971
## 9 lea_4 0.8415584# Calculate factor favorability score for engagement & leadership
## Extract factor abbreviations first
factor_abbreviations <- unique(sub("_.*", "", favorability_score$question_number)) #substitute "-.*" to " " then only save unique abbreviations
## Create an empty data frame to save the calculated factor_favorable_score for each factor
factor_favorable_score <- data.frame(factor_abbreviation = character(),
factor_favorable_score = numeric(),
stringsAsFactors = FALSE)
## Calculate the average favorable score for each factor by using this function:
for (factor_abbr in factor_abbreviations) {
# Subset favorable scores for questions belonging to the factor by using 'factor_abbreviations' vector
factor_scores <- favorability_score[grep(paste0("^", factor_abbr, "_"), favorability_score$question_number), ]
#use grep to find 'factor_abbre_" in favorability_score df
# Calculate the average favorable score for the factor
avg_favorable_score <- mean(factor_scores$favorability_score, na.rm = TRUE) # Add na.rm = TRUE to handle missing values
# Add each factor favorable score to the data frame
factor_favorable_score <- rbind(
factor_favorable_score,
data.frame(
factor_abbreviation = factor_abbr,
factor_favorable_score = avg_favorable_score,
country = "United States" # Add the country label
)
)
}
print(factor_favorable_score)## factor_abbreviation factor_favorable_score country
## 1 eng 0.7786955 United States
## 2 lea 0.8336043 United Statesfavorability_score$Country <- "United States"
#let's combine these two dfs
colnames(factor_favorable_score) <- c("question_number", "favorability_score", "Country")
combined_favorability_df <- rbind(favorability_score, factor_favorable_score)
combined_favorability_df <- combined_favorability_df[order(combined_favorability_df$question_number), ]
print(combined_favorability_df)## question_number favorability_score Country
## 10 eng 0.7786955 United States
## 1 eng_1 0.9153200 United States
## 2 eng_2 0.8312864 United States
## 3 eng_3 0.7692806 United States
## 4 eng_4 0.6735751 United States
## 5 eng_5 0.7040155 United States
## 11 lea 0.8336043 United States
## 6 lea_1 0.8000000 United States
## 7 lea_2 0.8273616 United States
## 8 lea_3 0.8654971 United States
## 9 lea_4 0.8415584 United States#let's only keep engagement scores
engagement_favorable_scores <- combined_favorability_df[1:6, ]
print(engagement_favorable_scores)## question_number favorability_score Country
## 10 eng 0.7786955 United States
## 1 eng_1 0.9153200 United States
## 2 eng_2 0.8312864 United States
## 3 eng_3 0.7692806 United States
## 4 eng_4 0.6735751 United States
## 5 eng_5 0.7040155 United States# Distinguish the average row from other item rows
engagement_favorable_scores$highlight <- ifelse(engagement_favorable_scores$question_number == engagement_favorable_scores $question_number[1], "first", "other")
# Create a color palette to specify the average factor favorable score from others
highlight_colors <- c("first" = "#5E81AC", # blue for the 'first' category
"other" = "#AEBAC2") # Grey for the 'other' category
library(scales)
# Create a ggplot that shows engagement factor favorable scores for U.S. employees
ggplot(engagement_favorable_scores, aes(x = question_number, y = favorability_score, fill = highlight)) +
geom_bar(stat = "identity", show.legend = FALSE, width = 0.6) +
labs(x = "Factor Items", y = "Favorability Score (%)", title = "Engagement Factor Favorable Scores for U.S. Employees") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 0, hjust = 0.5, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 16),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14)
) +
geom_text(aes(label = paste0(round(favorability_score * 100), "%")), vjust = -0.5, size = 4) +
scale_fill_manual(values = highlight_colors) +
scale_y_continuous(labels = percent_format(accuracy = 1)) 
3.6 Demographic Composition of the Company
3.6.1 By country
library(ggplot2)
library(dplyr)
country_df <- as.data.frame(table(df_2$country))
colnames(country_df) <- c("Country", "Count")
##bar graph
ggplot(country_df, aes(x = Country, y = Count, fill = Country)) +
geom_bar(stat = "identity") +
labs(
x = "Country",
y = "Employee Count",
title = paste("Employee Distribution Across Countries"),
caption = "Total: 2651 employees"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(hjust = 1, face = "italic")
) +
geom_text(aes(label = paste(Count)), vjust = -0.5, size = 3.5) 
# Calculate the proportion of employees for each country
country_df <- country_df %>%
mutate(Proportion = Count / sum(Count))
ggplot(country_df, aes(x = "", y = Proportion, fill = Country)) +
geom_bar(stat = "identity", width = 1) +
coord_polar(theta = "y") +
labs(
x = NULL,
y = NULL,
title = "Employee Distribution by Country (Percentage)"
) +
theme_minimal() +
theme(
axis.text.x = element_blank(),
panel.grid = element_blank()
) +
geom_text(aes(label = paste(Country, scales::percent(Proportion, accuracy = 0.1))),
position = position_stack(vjust = 0.5), # Center text on each slice
size = 3) 
# barchart in percentage
ggplot(country_df, aes(x = Country, y = Proportion*100, fill = Country)) +
geom_bar(stat = "identity") +
labs(
x = "Country",
y = "Proportion (%)",
title = "Employee Distribution by Country (Percentage)"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5)
) +
geom_text(aes(label = paste0(round(Proportion*100, 1), "%")),
vjust = -0.5,
size = 3.5) 
3.6.2 By gender
#by gender
gender_df <- as.data.frame(table(df_2$gender, useNA = "always"))
colnames(gender_df) <- c("Gender", "Count")
gender_df <- gender_df %>%
mutate(
Gender = case_when(
Gender == "Female" ~ "Women",
Gender == "Male" ~ "Men",
is.na(Gender) ~ "Unknown",
TRUE ~ Gender # Keep other values unchanged
)
)
gender_dfgender_df$Tenure <- factor(
gender_df$Gender,
levels = c("Men", "Women", "Unknown"))
total_employees <- sum(gender_df$Count)
ggplot(gender_df, aes(x = "", y = Count, fill = Gender)) +
geom_bar(stat = "identity", width = 1) +
coord_polar(theta = "y") +
labs(
x = NULL,
y = NULL,
title = paste("Employee Distribution Across Gender (Total:", total_employees, "employees)") # Add total employees in the title
) +
theme_minimal() +
theme(
axis.text.x = element_blank(),
panel.grid = element_blank()
) +
geom_text(aes(label = paste(Gender, " : ", Count)),
position = position_stack(vjust = 0.5),
size = 3.5)
##bar graph
ggplot(gender_df, aes(x = Gender, y = Count, fill = Gender)) +
geom_bar(stat = "identity") +
labs(
x = "Gender Group",
y = "Employee Count",
title = paste("Employee Distribution Across Gender Groups"),
caption = "Total: 2651 employees"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(hjust = 1, face = "italic")
) +
geom_text(aes(label = paste(Count)), vjust = -0.5, size = 3.5)
3.6.3 By tenure
#by tenure group
tenure_df <- as.data.frame(table(df_2$tenure_group))
colnames(tenure_df) <- c("Tenure", "Count")
tenure_df$Tenure <- factor(
tenure_df$Tenure,
levels = c("Under 3 months", "3-6 months", "6-12 months", "1-2 years", "2-4 years", "4-6 years", "6-10 years", "10+ years"))
tenure_dftotal_employees <- sum(tenure_df$Count)
##bar graph
ggplot(tenure_df, aes(x = Tenure, y = Count, fill = Tenure)) +
geom_bar(stat = "identity") +
labs(
x = "Tenure Group",
y = "Employee Count",
title = paste("Employee Distribution Across Tenure Groups"),
caption = "Total: 2651 employees"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(hjust = 1, face = "italic")
) +
geom_text(aes(label = paste(Count)), vjust = -0.5, size = 3) 
3.6.4 By tenure & gender
#by tenure & gender
library(ggplot2)
library(dplyr)
#Create separate data frames for men and women by tenure group
men_tenure_df <- as.data.frame(table(df_2$tenure_group[df_2$gender == "Male"]))
women_tenure_df <- as.data.frame(table(df_2$tenure_group[df_2$gender == "Female"]))
colnames(men_tenure_df) <- c("Tenure", "Count")
colnames(women_tenure_df) <- c("Tenure", "Count")
#Ensure the tenure groups are in the correct order
tenure_levels <- c("Under 3 months", "3-6 months", "6-12 months", "1-2 years", "2-4 years", "4-6 years", "6-10 years", "10+ years")
men_tenure_df$Tenure <- factor(men_tenure_df$Tenure, levels = tenure_levels)
women_tenure_df$Tenure <- factor(women_tenure_df$Tenure, levels = tenure_levels)
#Add a gender column to each data frame
men_tenure_df$Gender <- "Men"
women_tenure_df$Gender <- "Women"
#Combine the two data frames
combined_tenure_df <- rbind(men_tenure_df, women_tenure_df)
#Create a bar plot comparing men and women by tenure group
ggplot(combined_tenure_df, aes(x = Tenure, y = Count, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") + # Bars side-by-side
labs(
x = "Tenure Group",
y = "Employee Count",
title = "Employee Distribution by Tenure Group and Gender",
caption = "Total: 2651 employees"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.caption = element_text(hjust = 1, face = "italic")
) +
geom_text(aes(label = Count), position = position_dodge(width = 0.9), vjust = -0.5, size = 3) 